Intro

In this script, I load exchange data from datras and extract CA data (i.e. biological data), which is used for the condition model. In theory I could have joined the CPUE data to the CA data and get all important covariates and variables (oxygen, depth, ices information, densities of cod and flounder). However, the CPUE data has been standardized with respect to trawl speed, gear dimension, sweep length and trawl duration. Because many haul id’s did not have this information, the CPUE data has fewer id’s. In order to not lose condition data because I don’t have haul-level CPUE of cod and flounder, I will instead repeat the data cleaning process here, fit models to cod and flounder CPUE and predict at the location of the condition data.

Load libraries

rm(list = ls())

library(tidyverse); theme_set(theme_light(base_size = 12))
library(readxl)
library(tidylog)
library(RCurl)
library(viridis)
library(RColorBrewer)
library(patchwork)
library(janitor)
library(icesDatras)
library(mapdata)
library(patchwork)
library(rgdal)
library(raster)
library(sf)
library(rgeos)
library(chron)
library(lattice)
library(ncdf4)
library(marmap)
library(rnaturalearth)
library(rnaturalearthdata)
library(mapplots)
library(geosphere)
#remotes::install_github("pbs-assess/sdmTMB")
library(sdmTMB)

For maps

world <- ne_countries(scale = "medium", returnclass = "sf")

# Specify map ranges
ymin = 54; ymax = 58; xmin = 12; xmax = 22

map_data <- rnaturalearth::ne_countries(
  scale = "medium",
  returnclass = "sf", continent = "europe")

# Crop the polygon for plotting and efficiency:
# st_bbox(map_data) # find the rough coordinates
swe_coast <- suppressWarnings(suppressMessages(
  st_crop(map_data,
          c(xmin = xmin, ymin = ymin, xmax = xmax, ymax = ymax))))

# Transform our map into UTM 33 coordinates, which is the equal-area projection we fit in:
utm_zone33 <- 32633
swe_coast_proj <- sf::st_transform(swe_coast, crs = utm_zone33)

#ggplot(swe_coast_proj) + geom_sf() 

Read data

# Read HH data
bits_hh <- read.csv("data/DATRAS_exchange/bits_hh.csv") %>% filter(Quarter == 4)

# Read CA data
bits_ca <- read.csv("data/DATRAS_exchange/bits_ca.csv") %>% filter(Quarter == 4)

Standardize catch data

Standardize ships

# Before creating a a new ID, make sure that countries and ships names use the same format
sort(unique(bits_hh$Ship))
#>  [1] "06S1" "06SL" "26D4" "26HF" "26HI" "67BC" "77AR" "77SE" "AA36" "ESLF"
#> [11] "ESOR" "ESTM" "LTDA" "RUJB" "RUNT"

# Change back to the old Ship name standard...
# https://vocab.ices.dk/?ref=315
# https://vocab.ices.dk/?ref=315
# Assumptions:
# SOL is Solea on ICES links above, and SOL1 is the older one of the two SOLs (1 and 2)
# DAN is Dana
# sweep %>% filter(Ship == "DANS") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "67BC") %>% distinct(Year, Country)
# sweep %>% filter(Ship == "DAN2") %>% distinct(Year)
# bits_hh %>% filter(Ship == "26D4") %>% distinct(Year) # Strange that 26DF doesn't extend far back. Which ship did the Danes use? Ok, I have no Danish data that old.
# bits_hh %>% filter(Country == "DK") %>% distinct(Year)

bits_hh <- bits_hh %>%
  mutate(Ship2 = fct_recode(Ship,
                            "SOL" = "06S1", 
                            "SOL2" = "06SL",
                            "DAN2" = "26D4",
                            "HAF" = "26HF",
                            "HAF" = "26HI",
                            "HAF" = "67BC",
                            "BAL" = "67BC",
                            "ARG" = "77AR",
                            "77SE" = "77SE",
                            "AA36" = "AA36",
                            "KOOT" = "ESLF",
                            "KOH" = "ESTM",
                            "DAR" = "LTDA",
                            "ATLD" = "RUJB",
                            "ATL" = "RUNT"), 
         Ship2 = as.character(Ship2)) %>% 
  mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))

bits_ca <- bits_ca %>%
  mutate(Ship2 = fct_recode(Ship,
                            "SOL" = "06S1", 
                            "SOL2" = "06SL",
                            "DAN2" = "26D4",
                            "HAF" = "26HF",
                            "HAF" = "26HI",
                            "HAF" = "67BC",
                            "BAL" = "67BC",
                            "ARG" = "77AR",
                            "77SE" = "77SE",
                            "AA36" = "AA36",
                            "KOOT" = "ESLF",
                            "KOH" = "ESTM",
                            "DAR" = "LTDA",
                            "ATLD" = "RUJB",
                            "ATL" = "RUNT"), 
         Ship2 = as.character(Ship2)) %>% 
  mutate(Ship3 = ifelse(Country == "LV" & Ship2 == "BAL", "BALL", Ship2))

Standardize countries

# Now check which country codes are used
sort(unique(bits_hh$Country))
#> [1] "DE" "DK" "EE" "LT" "LV" "PL" "RU" "SE"

# https://www.nationsonline.org/oneworld/country_code_list.htm#E
bits_hh <- bits_hh %>%
  mutate(Country = fct_recode(Country,
                              "DEN" = "DK",
                              "EST" = "EE",
                              "GFR" = "DE",
                              "LAT" = "LV",
                              "LTU" = "LT",
                              "POL" = "PL",
                              "RUS" = "RU",
                              "SWE" = "SE"),
         Country = as.character(Country))

bits_ca <- bits_ca %>%
  mutate(Country = fct_recode(Country,
                              "DEN" = "DK",
                              "EST" = "EE",
                              "GFR" = "DE",
                              "LAT" = "LV",
                              "LTU" = "LT",
                              "POL" = "PL",
                              "RUS" = "RU",
                              "SWE" = "SE"),
         Country = as.character(Country))

# Gear? Are they the same?
sort(unique(bits_hh$Gear))
#>  [1] "ESB" "EXP" "FOT" "GOV" "GRT" "H20" "LBT" "PEL" "SON" "TVL" "TVS"

Create a simple ID that works across all exchange data

# Create ID column
bits_ca <- bits_ca %>% 
  mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))

bits_hh <- bits_hh %>% 
  mutate(IDx = paste(Year, Quarter, Country, Ship, Gear, StNo, HaulNo, sep = "."))

# Works like a haul-id
# bits_hh %>% group_by(IDx) %>% mutate(n = n()) %>% ungroup() %>% distinct(n)

Create the same unique haul-ID in the cpue data that I have in the sweep-file

bits_hh <- bits_hh %>% 
  mutate(haul.id = paste(Year, Quarter, Country, Ship3, Gear, StNo, HaulNo, sep = ":")) 

bits_hh %>% 
  distinct(haul.id, .keep_all = TRUE) %>% 
  group_by(Month) %>% 
  summarise(n = n()) %>% 
  ungroup() %>% 
  mutate((prop = n / sum(n)) * 100)
#> # A tibble: 4 × 3
#>   Month     n `(prop = n/sum(n)) * 100`
#>   <int> <int>                     <dbl>
#> 1     9    15                     0.230
#> 2    10   927                    14.2  
#> 3    11  4948                    75.9  
#> 4    12   633                     9.70

Clean DATRAS EXCHANGE data

# Select just valid, additional and no oxygen hauls
bits_hh <- bits_hh %>%
  filter(HaulVal %in% c("A","N","V"))

# Add ICES rectangle
bits_hh$Rect <- mapplots::ices.rect2(lon = bits_hh$ShootLong, lat = bits_hh$ShootLat)

# Add ICES subdivisions
shape <- shapefile("data/ICES_StatRec_mapto_ICES_Areas/StatRec_map_Areas_Full_20170124.shp")

pts <- SpatialPoints(cbind(bits_hh$ShootLong, bits_hh$ShootLat), 
                     proj4string = CRS(proj4string(shape)))
#> Warning in proj4string(shape): CRS object has comment, which is lost in output

bits_hh$sub_div <- over(pts, shape)$Area_27

# Rename subdivisions to the more common names and do some more filtering (by sub div and area)
sort(unique(bits_hh$sub_div))
#>  [1] "3.a.20"   "3.a.21"   "3.b.23"   "3.c.22"   "3.d.24"   "3.d.25"  
#>  [7] "3.d.26"   "3.d.27"   "3.d.28.1" "3.d.28.2" "3.d.29"

bits_hh <- bits_hh %>% 
  mutate(sub_div = factor(sub_div),
         sub_div = fct_recode(sub_div,
                              "20" = "3.a.20",
                              "21" = "3.a.21",
                              "22" = "3.c.22",
                              "23" = "3.b.23",
                              "24" = "3.d.24",
                              "25" = "3.d.25",
                              "26" = "3.d.26",
                              "27" = "3.d.27",
                              "28" = "3.d.28.1",
                              "28" = "3.d.28.2",
                              "29" = "3.d.29",
                              "30" = "3.d.30"),
         sub_div = as.character(sub_div)) 
#> Warning: Unknown levels in `f`: 3.d.30

# Match columns from the HH data to the HL and CA data
sort(unique(bits_hh$sub_div))
#>  [1] "20" "21" "22" "23" "24" "25" "26" "27" "28" "29"
sort(colnames(bits_hh))
#>  [1] "BotCurDir"         "BotCurSpeed"       "BotSal"           
#>  [4] "BotTemp"           "Buoyancy"          "BySpecRecCode"    
#>  [7] "CodendMesh"        "Country"           "DataType"         
#> [10] "DateofCalculation" "Day"               "DayNight"         
#> [13] "Depth"             "DepthStratum"      "Distance"         
#> [16] "DoorSpread"        "DoorSurface"       "DoorType"         
#> [19] "DoorWgt"           "Gear"              "GearEx"           
#> [22] "GroundSpeed"       "haul.id"           "HaulDur"          
#> [25] "HaulLat"           "HaulLong"          "HaulNo"           
#> [28] "HaulVal"           "HydroStNo"         "IDx"              
#> [31] "KiteDim"           "MaxTrawlDepth"     "MinTrawlDepth"    
#> [34] "Month"             "Netopening"        "PelSampType"      
#> [37] "Quarter"           "RecordType"        "Rect"             
#> [40] "Rigging"           "SecchiDepth"       "Ship"             
#> [43] "Ship2"             "Ship3"             "ShootLat"         
#> [46] "ShootLong"         "SpeedWater"        "StatRec"          
#> [49] "StdSpecRecCode"    "StNo"              "sub_div"          
#> [52] "SurCurDir"         "SurCurSpeed"       "SurSal"           
#> [55] "SurTemp"           "Survey"            "SweepLngt"        
#> [58] "SwellDir"          "SwellHeight"       "ThClineDepth"     
#> [61] "ThermoCline"       "Tickler"           "TidePhase"        
#> [64] "TideSpeed"         "TimeShot"          "TowDir"           
#> [67] "Turbidity"         "WarpDen"           "Warpdia"          
#> [70] "Warplngt"          "WgtGroundRope"     "WindDir"          
#> [73] "WindSpeed"         "WingSpread"        "X"                
#> [76] "Year"
bits_hh_merge <- bits_hh %>% 
                       dplyr::select(sub_div, Rect, HaulVal, StdSpecRecCode, BySpecRecCode,
                                     IDx, ShootLat, ShootLong)

bits_ca <- left_join(bits_ca, bits_hh_merge, by = "IDx")

# Now filter the subdivisions I want from all data sets
bits_hh <- bits_hh %>% filter(sub_div %in% c(24, 25, 26, 27, 28))
bits_ca <- bits_ca %>% filter(sub_div %in% c(24, 25, 26, 27, 28))

Now clean the CA data and go to 1 row 1 observation

bits_ca <- bits_ca %>% 
  filter(SpecCode %in% c("164712", "126436") & Year < 2020) %>% 
  mutate(Species = "Cod")

# Now I need to copy rows with NoAtLngt > 1 so that 1 row = 1 ind
# First make a small test
# nrow(bits_ca)
# test_id <- head(filter(bits_ca, CANoAtLngt == 5))$ID[1]
# filter(bits_ca, ID == test_id & CANoAtLngt == 5)

bits_ca <- bits_ca %>% map_df(., rep, .$CANoAtLngt)

# head(data.frame(filter(bits_ca, ID == test_id & CANoAtLngt == 5)), 20)
# nrow(bits_ca)
# Looks ok!

# Standardize length and drop NA weights (need that for condition)
bits_ca <- bits_ca %>% 
  drop_na(IndWgt) %>% 
  drop_na(LngtClass) %>% 
  filter(IndWgt > 0 & LngtClass > 0) %>%  # Filter positive length and weight
  mutate(weight_g = IndWgt) %>% 
  mutate(length_cm = ifelse(LngtCode == ".", 
                            LngtClass/10,
                            LngtClass)) # Standardize length (https://vocab.ices.dk/?ref=18)

# Plot
ggplot(bits_ca, aes(IndWgt, length_cm)) +
  geom_point() + 
  facet_wrap(~Year)


# Remove apparent outlier
bits_ca <- bits_ca %>%
  mutate(keep = ifelse(Year == 2010 & IndWgt > 12500, "N", "Y")) %>% 
  filter(keep == "Y") %>% dplyr::select(-keep)

ggplot(bits_ca, aes(IndWgt, length_cm)) +
  geom_point() + 
  facet_wrap(~Year)


# Rename things and select specific columns
dat <- bits_ca %>% rename("year" = "Year",
                          "lat" = "ShootLat",
                          "lon" = "ShootLong",
                          "quarter" = "Quarter",
                          "ices_rect" = "Rect") %>% 
  dplyr::select(weight_g, length_cm, year, lat, lon, quarter, IDx, ices_rect, sub_div)

# > nrow(dat)
# [1] 98863

dat %>% drop_na(weight_g) %>% filter(year > 1992) %>% ggplot(., aes(lon, lat)) + geom_point(size = 0.1) + facet_wrap(~year)

Add in the environmental variables

Depth

# Read the tifs
west <- raster("data/depth_geo_tif/D5_2018_rgb-1.tif")
#plot(west)

east <- raster("data/depth_geo_tif/D6_2018_rgb-1.tif")
# plot(east)

dep_rast <- raster::merge(west, east)

dat$depth <- extract(dep_rast, dat[, 5:4])

# Convert to depth (instead of elevation)
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

dat$depth <- (dat$depth - max(drop_na(dat)$depth)) *-1
#> drop_na: no rows removed
ggplot(dat, aes(depth)) + geom_histogram()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Oxygen

# Downloaded from here: https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=BALTICSEA_REANALYSIS_BIO_003_012
# Extract raster points: https://gisday.wordpress.com/2014/03/24/extract-raster-values-from-points-using-r/comment-page-1/
# https://rpubs.com/boyerag/297592
# https://pjbartlein.github.io/REarthSysSci/netCDF.html#get-a-variable
# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1610091357600.nc")

print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-scobi-monthlymeans_1610091357600.nc (NC_FORMAT_CLASSIC):
#> 
#>      1 variables (excluding dimension variables):
#>         float o2b[longitude,latitude,time]   
#>             long_name: Sea_floor_Dissolved_Oxygen_Concentration
#>             missing_value: NaN
#>             standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
#>             units: mmol m-3
#>             _FillValue: NaN
#>             _ChunkSizes: 1
#>              _ChunkSizes: 523
#>              _ChunkSizes: 383
#> 
#>      3 dimensions:
#>         time  Size:324
#>             axis: T
#>             long_name: Validity time
#>             standard_name: time
#>             units: days since 1950-01-01 00:00:00
#>             calendar: gregorian
#>             _ChunkSizes: 512
#>             _CoordinateAxisType: Time
#>             valid_min: 15721.5
#>             valid_max: 25551.5
#>         latitude  Size:166
#>             axis: Y
#>             standard_name: latitude
#>             long_name: latitude
#>             units: degrees_north
#>             _CoordinateAxisType: Lat
#>             valid_min: 52.991626739502
#>             valid_max: 58.4915390014648
#>         longitude  Size:253
#>             standard_name: longitude
#>             long_name: longitude
#>             units: degrees_east
#>             axis: X
#>             _CoordinateAxisType: Lon
#>             valid_min: 9.01375484466553
#>             valid_max: 23.013614654541
#> 
#>     24 global attributes:
#>         references: http://www.smhi.se
#>         institution: Swedish Meterological and Hydrological Institute
#>         history: See source and creation_date attributees
#>         Conventions: CF-1.5
#>         contact: servicedesk_cmems@mercator-ocean.eu
#>         comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#>         bullentin_type: reanalysis
#>         cmems_product_id: BALTICSEA_REANALYSIS_BIO_003_012
#>         title: CMEMS V4 Reanalysis: SCOBI model 3D fields (monthly means)
#>         FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#>         FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#>         FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#>         FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#>         shallowest_depth: 1.50136542320251
#>         deepest_depth: 711.059204101562
#>         source: SMHI reanalysis run NORDIC-NS2_1d_20191201_20191202
#>         file_quality_index: 1
#>         creation_date: 2020-11-16 UTC
#>         bullentin_date: 20191201
#>         start_date: 2019-12-01 UTC
#>         stop_date: 2019-12-01 UTC
#>         start_time: 00:00 UTC
#>         stop_time: 00:00 UTC
#>         _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention

# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530

lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 52.99163 53.02496 53.05829 53.09163 53.12496 53.15829

# Get time
time <- ncvar_get(ncin,"time")
time
#>   [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#>  [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#>  [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#>  [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#>  [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#>  [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#>  [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#>  [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#>  [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#>  [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#>  [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5

tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 324
tunits
#> $hasatt
#> [1] TRUE
#> 
#> $value
#> [1] "days since 1950-01-01 00:00:00"

# Get oxygen
dname <- "o2b"

oxy_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(oxy_array)
#> [1] 253 166 324

# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")

# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])

# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))

# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)

# Replace netCDF fill values with NA's
oxy_array[oxy_array == fillvalue$value] <- NA

# We only use Quarter 4 in this analysis, so now we want to loop through each time step,
# and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#>   [1]  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1
#>  [26]  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2
#>  [51]  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3
#>  [76]  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4
#> [101]  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5
#> [126]  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6
#> [151]  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7
#> [176]  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8
#> [201]  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9
#> [226] 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10
#> [251] 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11
#> [276] 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12
#> [301]  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12

index_keep <- which(months > 9)

oxy_q4 <- oxy_array[, , index_keep]

months_keep <- months[index_keep]

years_keep <- years[index_keep]

# Now we have an array with only Q4 data...
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq <- seq(1, dim(oxy_q4)[3], by = 3)

# Create objects that will hold data
dlist <- list()
oxy_10 <- c()
oxy_11 <- c()
oxy_12 <- c()
oxy_ave <- c()

# Loop through the vector sequence with every third value, then take the average of
# three consecutive months (i.e. q4)
for(i in loop_seq) {
  
  oxy_10 <- oxy_q4[, , (i)]
  oxy_11 <- oxy_q4[, , (i + 1)]
  oxy_12 <- oxy_q4[, , (i + 2)]
  
  oxy_ave <- (oxy_10 + oxy_11 + oxy_12) / 3
  
  list_pos <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
  
  dlist[[list_pos]] <- oxy_ave
  
}

# Now name the lists with the year:
names(dlist) <- unique(years_keep)

# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script

# Filter years in the cpue data frame to only have the years I have oxygen for
d_sub_oxy <- dat %>% filter(year %in% names(dlist)) %>% droplevels()
#> filter: removed 1,305 rows (1%), 97,558 rows remaining

# Create data holding object
data_list <- list()

# ... And for the oxygen raster
raster_list <- list()

# Create factor year for indexing the list in the loop
d_sub_oxy$year_f <- as.factor(d_sub_oxy$year)

# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_oxy$year_f)) {
  
  # Set plot limits
  ymin = 54; ymax = 58; xmin = 12; xmax = 22

  # Subset a year
  oxy_slice <- dlist[[i]]
  
  # Create raster for that year (i)
  r <- raster(t(oxy_slice), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
              crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
  
  # Flip...
  r <- flip(r, direction = 'y')
  
  #plot(r, main = i)
  
  # Filter the same year (i) in the cpue data and select only coordinates
  d_slice <- d_sub_oxy %>% filter(year_f == i) %>% dplyr::select(lon, lat)
  
  # Make into a SpatialPoints object
  data_sp <- SpatialPoints(d_slice)
  
  # Extract raster value (oxygen)
  rasValue <- raster::extract(r, data_sp)
  
  # Now we want to plot the results of the raster extractions by plotting the cpue
  # data points over a raster and saving it for each year.
  # Make the SpatialPoints object into a raster again (for pl)
  df <- as.data.frame(data_sp)
  
  # Add in the raster value in the df holding the coordinates for the cpue data
  d_slice$oxy <- rasValue
  
  # Add in which year
  d_slice$year <- i
  
  # Create a index for the data last where we store all years (because our loop index
  # i is not continuous, we can't use it directly)
  index <- as.numeric(d_slice$year)[1] - 1992
  
  # Add each years' data in the list
  data_list[[index]] <- d_slice
  
  # Save to check each year is ok! First convert the raster to points for plotting
  # (so that we can use ggplot)
  map.p <- rasterToPoints(r)
  
  # Make the points a dataframe for ggplot
  df_rast <- data.frame(map.p)
  
  # Rename y-variable and add year
  df_rast <- df_rast %>% rename("oxy" = "layer") %>% mutate(year = i)
  
  # Add each years' raster data frame in the list
  raster_list[[index]] <- df_rast
  
  # Make appropriate column headings
  colnames(df_rast) <- c("Longitude", "Latitude", "oxy")
  
  # Now make the map
  ggplot(data = df_rast, aes(y = Latitude, x = Longitude)) +
    geom_raster(aes(fill = oxy)) +
    geom_point(data = d_slice, aes(x = lon, y = lat, fill = oxy),
               color = "black", size = 5, shape = 21) +
    theme_bw() +
    geom_sf(data = world, inherit.aes = F, size = 0.2) +
    coord_sf(xlim = c(xmin, xmax),
             ylim = c(ymin, ymax)) +
    scale_colour_gradientn(colours = rev(terrain.colors(10)),
                           limits = c(-200, 400)) +
    scale_fill_gradientn(colours = rev(terrain.colors(10)),
                         limits = c(-200, 400)) +
    NULL
  
  ggsave(paste("figures/supp/cpue_oxygen_rasters/", i,".png", sep = ""),
         width = 6.5, height = 6.5, dpi = 600)
  
}
#> filter: removed 96,361 rows (99%), 1,197 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,731 rows (98%), 1,827 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,528 rows (98%), 2,030 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,874 rows (98%), 1,684 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,471 rows (98%), 2,087 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,757 rows (98%), 1,801 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,653 rows (98%), 1,905 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,414 rows (97%), 3,144 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,525 rows (98%), 2,033 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,631 rows (98%), 1,927 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,149 rows (97%), 3,409 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,678 rows (95%), 4,880 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,720 rows (94%), 5,838 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,896 rows (94%), 5,662 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 90,570 rows (93%), 6,988 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,253 rows (94%), 6,305 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,495 rows (96%), 4,063 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,789 rows (95%), 4,769 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,475 rows (96%), 4,083 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,686 rows (97%), 2,872 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,286 rows (97%), 3,272 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,349 rows (96%), 4,209 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,298 rows (96%), 4,260 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,379 rows (94%), 6,179 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,746 rows (95%), 4,812 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,270 rows (97%), 3,288 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,524 rows (97%), 3,034 rows remaining
#> rename: renamed one variable (oxy)
#> mutate: new variable 'year' (character) with one unique value and 0% NA

# Now create a data frame from the list of all annual values
big_dat_oxy <- dplyr::bind_rows(data_list)
big_raster_dat_oxy <- dplyr::bind_rows(raster_list)

# Plot data, looks like there's big inter-annual variation but a negative trend over time
big_raster_dat_oxy %>%
  group_by(year) %>%
  drop_na(oxy) %>%
  summarise(mean_oxy = mean(oxy)) %>%
  mutate(year_num = as.numeric(year)) %>%
  ggplot(., aes(year_num, mean_oxy)) +
  geom_point(size = 2) +
  stat_smooth(method = "lm") +
  NULL
#> group_by: one grouping variable (year)
#> drop_na (grouped): no rows removed
#> summarise: now 27 rows and 2 columns, ungrouped
#> mutate: new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'


big_raster_dat_oxy %>%
  group_by(year) %>%
  drop_na(oxy) %>%
  mutate(dead = ifelse(oxy < 0, "Y", "N")) %>%
  filter(dead == "Y") %>%
  mutate(n = n(),
         year_num = as.numeric(year)) %>%
  ggplot(., aes(year_num, n)) +
  geom_point(size = 2) +
  stat_smooth(method = "lm") +
  NULL
#> group_by: one grouping variable (year)
#> drop_na (grouped): no rows removed
#> mutate (grouped): new variable 'dead' (character) with 2 unique values and 0% NA
#> filter (grouped): removed 437,238 rows (93%), 31,671 rows remaining
#> mutate (grouped): new variable 'n' (integer) with 27 unique values and 0% NA
#>                   new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'


# Now add in the new oxygen column in the original data:
str(d_sub_oxy)
#> tibble [97,558 × 11] (S3: tbl_df/tbl/data.frame)
#>  $ weight_g : num [1:97558] 11 120 1416 25 733 ...
#>  $ length_cm: num [1:97558] 10 22 51 14 41 41 42 43 43 43 ...
#>  $ year     : int [1:97558] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#>  $ lat      : num [1:97558] 54.7 54.7 54.7 54.5 54.5 ...
#>  $ lon      : num [1:97558] 13.1 13.1 13.1 14.3 14.3 ...
#>  $ quarter  : int [1:97558] 4 4 4 4 4 4 4 4 4 4 ...
#>  $ IDx      : chr [1:97558] "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.22.43" ...
#>  $ ices_rect: chr [1:97558] "38G3" "38G3" "38G3" "38G4" ...
#>  $ sub_div  : chr [1:97558] "24" "24" "24" "24" ...
#>  $ depth    : num [1:97558] 8 8 8 7 7 7 7 7 7 7 ...
#>  $ year_f   : Factor w/ 27 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_oxy)
#> tibble [97,558 × 4] (S3: tbl_df/tbl/data.frame)
#>  $ lon : num [1:97558] 13.1 13.1 13.1 14.3 14.3 ...
#>  $ lat : num [1:97558] 54.7 54.7 54.7 54.5 54.5 ...
#>  $ oxy : num [1:97558] 324 324 324 349 349 ...
#>  $ year: chr [1:97558] "1993" "1993" "1993" "1993" ...

# Create an ID for matching the oxygen data with the cpue data
dat$id_oxy <- paste(dat$year, dat$lon, dat$lat, sep = "_")
big_dat_oxy$id_oxy <- paste(big_dat_oxy$year, big_dat_oxy$lon, big_dat_oxy$lat, sep = "_")

# Which id's are not in the cpue data (dat)?
ids <- dat$id_oxy[!dat$id_oxy %in% c(big_dat_oxy$id_oxy)]

unique(ids)
#>  [1] "1991_13.8_54.5167"    "1991_13.45_54.75"     "1991_13.5667_54.7167"
#>  [4] "1991_13.6667_54.6833" "1991_13.8_54.65"      "1991_13.85_54.7833"  
#>  [7] "1991_13.7_54.75"      "1991_13.95_54.9333"   "1991_13.6833_54.9833"
#> [10] "1991_13.1167_54.95"   "1991_13.2435_54.9697" "1991_14.15_54.5167"  
#> [13] "1991_14.0333_54.85"   "1991_14.1167_54.6667" "1991_14.0833_54.5833"
#> [16] "1991_14.25_54.6"      "1991_13.4833_55.0167" "1991_13.6_55.0333"   
#> [19] "1991_13.85_55.1"      "1991_13.9042_55.0183" "1991_13.2772_55.21"  
#> [22] "1991_13.6492_55.2135" "1991_15.6596_55.4523" "1991_15.3273_55.9365"
#> [25] "1991_15.3685_55.95"   "1991_15.9797_55.8632" "1991_16.0537_55.7407"
#> [28] "1991_16.431_55.5713"  "1991_16.4383_55.6667" "1991_17.668_55.8398" 
#> [31] "1991_17.0363_55.9378" "1991_17.7227_56.0398" "1991_18.4345_56.224" 
#> [34] "1991_18.7987_57.1543" "1991_18.9297_57.112"  "1991_18.8237_57.0395"
#> [37] "1991_19.1567_57.3528" "1991_14.4605_55.4568" "1991_14.2167_55.15"  
#> [40] "1991_14.2833_55.1833" "1991_14.2833_55"      "1991_14.488_55.6708" 
#> [43] "1991_14.7052_55.574"  "1991_17.5455_57.496"  "1991_18.1203_57.8185"
#> [46] "1991_19.406_57.8703"  "1991_19.5915_57.8896" "1991_17.0417_57.5"   
#> [49] "1992_13.6333_55"      "1992_14.1667_55.1333" "1992_14.2833_55.1833"
#> [52] "1992_13.1167_54.7167" "1992_13.3333_54.7"    "1992_13.8_54.5167"   
#> [55] "1992_13.55_54.7333"   "1992_13.9_54.5"       "1992_13.65_54.6833"  
#> [58] "1992_13.8_54.6333"    "1992_14.3_55.0333"    "1992_14.35_55.1"     
#> [61] "1992_13.8167_54.75"   "1992_13.7_54.75"      "1992_13.2_54.8667"   
#> [64] "1992_13.1_54.9167"    "1992_13.1167_54.9833" "1992_13.1167_54.65"  
#> [67] "1992_13.8833_54.5667" "1992_13.9667_54.9333" "1992_14.2167_54.6333"
#> [70] "1992_14.3667_54.5167" "1992_14.2_54.5"       "1992_14.0333_54.8667"
#> [73] "1992_14.1_54.7"       "1992_14.1333_54.5667" "1992_13.45_55.0167"  
#> [76] "1992_13.7667_55.1"    "1992_13.55_55.0333"   "1992_13.8667_55.1"

# Select only the columns we want to merge
big_dat_sub_oxy <- big_dat_oxy %>% dplyr::select(id_oxy, oxy)

# Remove duplicate ID (one oxy value per id)
big_dat_sub_oxy2 <- big_dat_sub_oxy %>% distinct(id_oxy, .keep_all = TRUE)
#> distinct: removed 94,317 rows (97%), 3,241 rows remaining
# big_dat_sub_oxy %>% group_by(id_oxy) %>% mutate(n = n()) %>% arrange(desc(n))

# Join the data with raster-derived oxygen with the full cpue data
dat <- left_join(dat, big_dat_sub_oxy2, by = "id_oxy")
#> left_join: added one column (oxy)
#>            > rows only in x    1,305
#>            > rows only in y  (     0)
#>            > matched rows     97,558
#>            >                 ========
#>            > rows total       98,863

# Now the unit of oxygen is mmol/m3. I want it to be ml/L. The original model is in unit ml/L
# and it's been converted by the data host. Since it was converted without accounting for
# pressure or temperature, I can simply use the following conversion factor:
# 1 ml/l = 103/22.391 = 44.661 μmol/l -> 1 ml/l = 0.044661 mmol/l = 44.661 mmol/m^3 -> 0.0223909 ml/l = 1mmol/m^3
# https://ocean.ices.dk/tools/unitconversion.aspx

dat$oxy <- dat$oxy * 0.0223909

# Drop NA oxygen
dat <- dat %>% drop_na(oxy)
#> drop_na: removed 1,419 rows (1%), 97,444 rows remaining

Temperature

# Open the netCDF file
ncin <- nc_open("data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1608127623694.nc")

print(ncin)
#> File data/NEMO_Nordic_SCOBI/dataset-reanalysis-nemo-monthlymeans_1608127623694.nc (NC_FORMAT_CLASSIC):
#> 
#>      1 variables (excluding dimension variables):
#>         float bottomT[longitude,latitude,time]   
#>             standard_name: sea_water_potential_temperature_at_sea_floor
#>             units: degrees_C
#>             long_name: Sea floor potential temperature
#>             missing_value: NaN
#>             _FillValue: NaN
#>             _ChunkSizes: 1
#>              _ChunkSizes: 523
#>              _ChunkSizes: 383
#> 
#>      3 dimensions:
#>         time  Size:324
#>             axis: T
#>             long_name: Validity time
#>             standard_name: time
#>             units: days since 1950-01-01 00:00:00
#>             calendar: gregorian
#>             _ChunkSizes: 512
#>             _CoordinateAxisType: Time
#>             valid_min: 15721.5
#>             valid_max: 25551.5
#>         latitude  Size:523
#>             axis: Y
#>             standard_name: latitude
#>             long_name: latitude
#>             units: degrees_north
#>             _CoordinateAxisType: Lat
#>             valid_min: 48.49169921875
#>             valid_max: 65.8914184570312
#>         longitude  Size:383
#>             standard_name: longitude
#>             long_name: longitude
#>             units: degrees_east
#>             axis: X
#>             _CoordinateAxisType: Lon
#>             valid_min: 9.01375484466553
#>             valid_max: 30.2357654571533
#> 
#>     24 global attributes:
#>         references: http://www.smhi.se
#>         institution: Swedish Meterological and Hydrological Institute
#>         history: See source and creation_date attributees
#>         Conventions: CF-1.5
#>         contact: servicedesk_cmems@mercator-ocean.eu
#>         comment: Provided by SMHI as a Copernicus Marine Environment Monitoring Service production unit
#>         bullentin_type: reanalysis
#>         cmems_product_id: BALTICSEA_REANALYSIS_PHY_003_011
#>         title: CMEMS V4 Reanalysis: NEMO model 3D fields (monthly means)
#>         FROM_ORIGINAL_FILE__easternmost_longitude: 30.2357654571533
#>         FROM_ORIGINAL_FILE__northernmost_latitude: 65.8914184570312
#>         FROM_ORIGINAL_FILE__westernmost_longitude: 9.01375484466553
#>         FROM_ORIGINAL_FILE__southernmost_latitude: 48.49169921875
#>         shallowest_depth: 1.50136542320251
#>         deepest_depth: 711.059204101562
#>         source: SMHI reanalysis run NORDIC-NS2_1d_20191201_20191202
#>         file_quality_index: 1
#>         creation_date: 2020-11-16 UTC
#>         bullentin_date: 20191201
#>         start_date: 2019-12-01 UTC
#>         stop_date: 2019-12-01 UTC
#>         start_time: 00:00 UTC
#>         stop_time: 00:00 UTC
#>         _CoordSysBuilder: ucar.nc2.dataset.conv.CF1Convention

# Get longitude and latitude
lon <- ncvar_get(ncin,"longitude")
nlon <- dim(lon)
head(lon)
#> [1] 9.013755 9.069310 9.124865 9.180420 9.235975 9.291530

lat <- ncvar_get(ncin,"latitude")
nlat <- dim(lat)
head(lat)
#> [1] 48.49170 48.52503 48.55836 48.59170 48.62503 48.65836

# Get time
time <- ncvar_get(ncin,"time")
time
#>   [1] 15721.5 15751.0 15780.5 15811.0 15841.5 15872.0 15902.5 15933.5 15964.0
#>  [10] 15994.5 16025.0 16055.5 16086.5 16116.0 16145.5 16176.0 16206.5 16237.0
#>  [19] 16267.5 16298.5 16329.0 16359.5 16390.0 16420.5 16451.5 16481.0 16510.5
#>  [28] 16541.0 16571.5 16602.0 16632.5 16663.5 16694.0 16724.5 16755.0 16785.5
#>  [37] 16816.5 16846.5 16876.5 16907.0 16937.5 16968.0 16998.5 17029.5 17060.0
#>  [46] 17090.5 17121.0 17151.5 17182.5 17212.0 17241.5 17272.0 17302.5 17333.0
#>  [55] 17363.5 17394.5 17425.0 17455.5 17486.0 17516.5 17547.5 17577.0 17606.5
#>  [64] 17637.0 17667.5 17698.0 17728.5 17759.5 17790.0 17820.5 17851.0 17881.5
#>  [73] 17912.5 17942.0 17971.5 18002.0 18032.5 18063.0 18093.5 18124.5 18155.0
#>  [82] 18185.5 18216.0 18246.5 18277.5 18307.5 18337.5 18368.0 18398.5 18429.0
#>  [91] 18459.5 18490.5 18521.0 18551.5 18582.0 18612.5 18643.5 18673.0 18702.5
#> [100] 18733.0 18763.5 18794.0 18824.5 18855.5 18886.0 18916.5 18947.0 18977.5
#> [109] 19008.5 19038.0 19067.5 19098.0 19128.5 19159.0 19189.5 19220.5 19251.0
#> [118] 19281.5 19312.0 19342.5 19373.5 19403.0 19432.5 19463.0 19493.5 19524.0
#> [127] 19554.5 19585.5 19616.0 19646.5 19677.0 19707.5 19738.5 19768.5 19798.5
#> [136] 19829.0 19859.5 19890.0 19920.5 19951.5 19982.0 20012.5 20043.0 20073.5
#> [145] 20104.5 20134.0 20163.5 20194.0 20224.5 20255.0 20285.5 20316.5 20347.0
#> [154] 20377.5 20408.0 20438.5 20469.5 20499.0 20528.5 20559.0 20589.5 20620.0
#> [163] 20650.5 20681.5 20712.0 20742.5 20773.0 20803.5 20834.5 20864.0 20893.5
#> [172] 20924.0 20954.5 20985.0 21015.5 21046.5 21077.0 21107.5 21138.0 21168.5
#> [181] 21199.5 21229.5 21259.5 21290.0 21320.5 21351.0 21381.5 21412.5 21443.0
#> [190] 21473.5 21504.0 21534.5 21565.5 21595.0 21624.5 21655.0 21685.5 21716.0
#> [199] 21746.5 21777.5 21808.0 21838.5 21869.0 21899.5 21930.5 21960.0 21989.5
#> [208] 22020.0 22050.5 22081.0 22111.5 22142.5 22173.0 22203.5 22234.0 22264.5
#> [217] 22295.5 22325.0 22354.5 22385.0 22415.5 22446.0 22476.5 22507.5 22538.0
#> [226] 22568.5 22599.0 22629.5 22660.5 22690.5 22720.5 22751.0 22781.5 22812.0
#> [235] 22842.5 22873.5 22904.0 22934.5 22965.0 22995.5 23026.5 23056.0 23085.5
#> [244] 23116.0 23146.5 23177.0 23207.5 23238.5 23269.0 23299.5 23330.0 23360.5
#> [253] 23391.5 23421.0 23450.5 23481.0 23511.5 23542.0 23572.5 23603.5 23634.0
#> [262] 23664.5 23695.0 23725.5 23756.5 23786.0 23815.5 23846.0 23876.5 23907.0
#> [271] 23937.5 23968.5 23999.0 24029.5 24060.0 24090.5 24121.5 24151.5 24181.5
#> [280] 24212.0 24242.5 24273.0 24303.5 24334.5 24365.0 24395.5 24426.0 24456.5
#> [289] 24487.5 24517.0 24546.5 24577.0 24607.5 24638.0 24668.5 24699.5 24730.0
#> [298] 24760.5 24791.0 24821.5 24852.5 24882.0 24911.5 24942.0 24972.5 25003.0
#> [307] 25033.5 25064.5 25095.0 25125.5 25156.0 25186.5 25217.5 25247.0 25276.5
#> [316] 25307.0 25337.5 25368.0 25398.5 25429.5 25460.0 25490.5 25521.0 25551.5

tunits <- ncatt_get(ncin,"time","units")
nt <- dim(time)
nt
#> [1] 324
tunits
#> $hasatt
#> [1] TRUE
#> 
#> $value
#> [1] "days since 1950-01-01 00:00:00"

# Get temperature
dname <- "bottomT"

temp_array <- ncvar_get(ncin,dname)
dlname <- ncatt_get(ncin,dname,"long_name")
dunits <- ncatt_get(ncin,dname,"units")
fillvalue <- ncatt_get(ncin,dname,"_FillValue")
dim(temp_array)
#> [1] 383 523 324

# Get global attributes
title <- ncatt_get(ncin,0,"title")
institution <- ncatt_get(ncin,0,"institution")
datasource <- ncatt_get(ncin,0,"source")
references <- ncatt_get(ncin,0,"references")
history <- ncatt_get(ncin,0,"history")
Conventions <- ncatt_get(ncin,0,"Conventions")

# Convert time: split the time units string into fields
tustr <- strsplit(tunits$value, " ")
tdstr <- strsplit(unlist(tustr)[3], "-")
tmonth <- as.integer(unlist(tdstr)[2])
tday <- as.integer(unlist(tdstr)[3])
tyear <- as.integer(unlist(tdstr)[1])

# Here I deviate from the guide a little bit. Save this info:
dates <- chron(time, origin = c(tmonth, tday, tyear))

# Crop the date variable
months <- as.numeric(substr(dates, 2, 3))
years <- as.numeric(substr(dates, 8, 9))
years <- ifelse(years > 90, 1900 + years, 2000 + years)

# Replace netCDF fill values with NA's
temp_array[temp_array == fillvalue$value] <- NA

# We only use Quarter 4 in this analysis, so now we want to loop through each time step,
# and if it is a good month save it as a raster.
# First get the index of months that correspond to Q4
months
#>   [1]  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1
#>  [26]  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2
#>  [51]  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3
#>  [76]  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4
#> [101]  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5
#> [126]  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6
#> [151]  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7
#> [176]  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8
#> [201]  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9
#> [226] 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10
#> [251] 11 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11
#> [276] 12  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12
#> [301]  1  2  3  4  5  6  7  8  9 10 11 12  1  2  3  4  5  6  7  8  9 10 11 12

index_keep <- which(months > 9)

# Quarter 4 by keeping months in index_keep
temp_q4 <- temp_array[, , index_keep]

months_keep <- months[index_keep]

years_keep <- years[index_keep]

# Now we have an array with only Q4 data...
# We need to now calculate the average within a year.
# Get a sequence that takes every third value between 1: number of months (length)
loop_seq <- seq(1, dim(temp_q4)[3], by = 3)

# Create objects that will hold data
dlist <- list()
temp_10 <- c()
temp_11 <- c()
temp_12 <- c()
temp_ave <- c()

# Loop through the vector sequence with every third value, then take the average of
# three consecutive months (i.e. q4)
for(i in loop_seq) {
  
  temp_10 <- temp_q4[, , (i)]
  temp_11 <- temp_q4[, , (i + 1)]
  temp_12 <- temp_q4[, , (i + 2)]
  
  temp_ave <- (temp_10 + temp_11 + temp_12) / 3
  
  list_pos <- ((i/3) - (1/3)) + 1 # to get index 1:n(years)
  
  dlist[[list_pos]] <- temp_ave
  
}

# Now name the lists with the year:
names(dlist) <- unique(years_keep)

# Now I need to make a loop where I extract the raster value for each year...
# The cpue data is called dat so far in this script

# Filter years in the cpue data frame to only have the years I have temperature for
d_sub_temp <- dat %>% filter(year %in% names(dlist)) %>% droplevels()
#> filter: no rows removed

# Create data holding object
data_list <- list()

# ... And for the temperature raster
raster_list <- list()

# Create factor year for indexing the list in the loop
d_sub_temp$year_f <- as.factor(d_sub_temp$year)

# Loop through each year and extract raster values for the cpue data points
for(i in unique(d_sub_temp$year_f)) {
  
  # Subset a year
  temp_slice <- dlist[[i]]
  
  # Create raster for that year (i)
  r <- raster(t(temp_slice), xmn = min(lon), xmx = max(lon), ymn = min(lat), ymx = max(lat),
              crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0"))
  
  # Flip...
  r <- flip(r, direction = 'y')
  
  #plot(r, main = i)
  
  # Filter the same year (i) in the cpue data and select only coordinates
  d_slice <- d_sub_temp %>% filter(year_f == i) %>% dplyr::select(lon, lat)
  
  # Make into a SpatialPoints object
  data_sp <- SpatialPoints(d_slice)
  
  # Extract raster value (temperature)
  rasValue <- raster::extract(r, data_sp)
  
  # Now we want to plot the results of the raster extractions by plotting the cpue
  # data points over a raster and saving it for each year.
  # Make the SpatialPoints object into a raster again (for pl)
  df <- as.data.frame(data_sp)
  
  # Add in the raster value in the df holding the coordinates for the cpue data
  d_slice$temp <- rasValue
  
  # Add in which year
  d_slice$year <- i
  
  # Create a index for the data last where we store all years (because our loop index
  # i is not continuous, we can't use it directly)
  index <- as.numeric(d_slice$year)[1] - 1992
  
  # Add each years' data in the list
  data_list[[index]] <- d_slice
  
  # Save to check each year is ok! First convert the raster to points for plotting
  # (so that we can use ggplot)
  map.p <- rasterToPoints(r)
  
  # Make the points a dataframe for ggplot
  df_rast <- data.frame(map.p)
  
  # Rename y-variable and add year
  df_rast <- df_rast %>% rename("temp" = "layer") %>% mutate(year = i)
  
  # Add each years' raster data frame in the list
  raster_list[[index]] <- df_rast
  
  # Make appropriate column headings
  colnames(df_rast) <- c("Longitude", "Latitude", "temp")
  
  # Now make the map
  ggplot(data = df_rast, aes(y = Latitude, x = Longitude)) +
    geom_raster(aes(fill = temp)) +
    geom_point(data = d_slice, aes(x = lon, y = lat, fill = temp),
               color = "black", size = 5, shape = 21) +
    theme_bw() +
    geom_sf(data = world, inherit.aes = F, size = 0.2) +
    coord_sf(xlim = c(min(dat$lon), max(dat$lon)),
             ylim = c(min(dat$lat), max(dat$lat))) +
    scale_colour_gradientn(colours = rev(terrain.colors(10)),
                           limits = c(2, 17)) +
    scale_fill_gradientn(colours = rev(terrain.colors(10)),
                         limits = c(2, 17)) +
    NULL
  
  ggsave(paste("figures/supp/cpue_temp_rasters/", i,".png", sep = ""),
         width = 6.5, height = 6.5, dpi = 600)
  
}
#> filter: removed 96,260 rows (99%), 1,184 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,617 rows (98%), 1,827 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,414 rows (98%), 2,030 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,760 rows (98%), 1,684 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,357 rows (98%), 2,087 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,643 rows (98%), 1,801 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,539 rows (98%), 1,905 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,300 rows (97%), 3,144 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,411 rows (98%), 2,033 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 95,517 rows (98%), 1,927 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,035 rows (97%), 3,409 rows remaining
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#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,577 rows (95%), 4,867 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,606 rows (94%), 5,838 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,782 rows (94%), 5,662 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 90,456 rows (93%), 6,988 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,153 rows (94%), 6,291 rows remaining
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#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,444 rows (96%), 4,000 rows remaining
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#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,361 rows (96%), 4,083 rows remaining
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#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,573 rows (97%), 2,871 rows remaining
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#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,172 rows (97%), 3,272 rows remaining
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#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,243 rows (96%), 4,201 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 93,184 rows (96%), 4,260 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 91,265 rows (94%), 6,179 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 92,632 rows (95%), 4,812 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,156 rows (97%), 3,288 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA
#> filter: removed 94,410 rows (97%), 3,034 rows remaining
#> rename: renamed one variable (temp)
#> mutate: new variable 'year' (character) with one unique value and 0% NA

# Now create a data frame from the list of all annual values
big_dat_temp <- dplyr::bind_rows(data_list)
big_raster_dat_temp <- dplyr::bind_rows(raster_list)

big_dat_temp %>% drop_na(temp) %>% summarise(max = max(temp))
#> drop_na: no rows removed
#> summarise: now one row and one column, ungrouped
#> # A tibble: 1 × 1
#>     max
#>   <dbl>
#> 1  14.5
big_dat_temp %>% drop_na(temp) %>% summarise(min = min(temp))
#> drop_na: no rows removed
#> summarise: now one row and one column, ungrouped
#> # A tibble: 1 × 1
#>     min
#>   <dbl>
#> 1  3.53

# Plot data, looks like there's big inter-annual variation but a positive trend
big_raster_dat_temp %>%
  group_by(year) %>%
  drop_na(temp) %>%
  summarise(mean_temp = mean(temp)) %>%
  mutate(year_num = as.numeric(year)) %>%
  ggplot(., aes(year_num, mean_temp)) +
  geom_point(size = 2) +
  stat_smooth(method = "lm") +
  NULL
#> group_by: one grouping variable (year)
#> drop_na (grouped): no rows removed
#> summarise: now 27 rows and 2 columns, ungrouped
#> mutate: new variable 'year_num' (double) with 27 unique values and 0% NA
#> `geom_smooth()` using formula 'y ~ x'


# Now add in the new temperature column in the original data:
str(d_sub_temp)
#> tibble [97,444 × 13] (S3: tbl_df/tbl/data.frame)
#>  $ weight_g : num [1:97444] 11 120 1416 25 733 ...
#>  $ length_cm: num [1:97444] 10 22 51 14 41 41 42 43 43 43 ...
#>  $ year     : int [1:97444] 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 ...
#>  $ lat      : num [1:97444] 54.7 54.7 54.7 54.5 54.5 ...
#>  $ lon      : num [1:97444] 13.1 13.1 13.1 14.3 14.3 ...
#>  $ quarter  : int [1:97444] 4 4 4 4 4 4 4 4 4 4 ...
#>  $ IDx      : chr [1:97444] "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.21.65" "1993.4.GFR.06S1.H20.22.43" ...
#>  $ ices_rect: chr [1:97444] "38G3" "38G3" "38G3" "38G4" ...
#>  $ sub_div  : chr [1:97444] "24" "24" "24" "24" ...
#>  $ depth    : num [1:97444] 8 8 8 7 7 7 7 7 7 7 ...
#>  $ id_oxy   : chr [1:97444] "1993_13.05_54.6667" "1993_13.05_54.6667" "1993_13.05_54.6667" "1993_14.3167_54.5167" ...
#>  $ oxy      : num [1:97444] 7.25 7.25 7.25 7.82 7.82 ...
#>  $ year_f   : Factor w/ 27 levels "1993","1994",..: 1 1 1 1 1 1 1 1 1 1 ...
str(big_dat_temp)
#> tibble [97,444 × 4] (S3: tbl_df/tbl/data.frame)
#>  $ lon : num [1:97444] 13.1 13.1 13.1 14.3 14.3 ...
#>  $ lat : num [1:97444] 54.7 54.7 54.7 54.5 54.5 ...
#>  $ temp: num [1:97444] 7.72 7.72 7.72 7.43 7.43 ...
#>  $ year: chr [1:97444] "1993" "1993" "1993" "1993" ...

# Create an ID for matching the temperature data with the cpue data
dat$id_temp <- paste(dat$year, dat$lon, dat$lat, sep = "_")
big_dat_temp$id_temp <- paste(big_dat_temp$year, big_dat_temp$lon, big_dat_temp$lat, sep = "_")

# Which id's are not in the cpue data (dat)? (It's because I don't have those years, not about the location)
ids <- dat$id_temp[!dat$id_temp %in% c(big_dat_temp$id_temp)]

unique(ids)
#> character(0)

# Select only the columns we want to merge
big_dat_sub_temp <- big_dat_temp %>% dplyr::select(id_temp, temp)

# Remove duplicate ID (one temp value per id)
big_dat_sub_temp2 <- big_dat_sub_temp %>% distinct(id_temp, .keep_all = TRUE)
#> distinct: removed 94,211 rows (97%), 3,233 rows remaining

# Join the data with raster-derived oxygen with the full cpue data
dat <- left_join(dat, big_dat_sub_temp2, by = "id_temp")
#> left_join: added one column (temp)
#>            > rows only in x        0
#>            > rows only in y  (     0)
#>            > matched rows     97,444
#>            >                 ========
#>            > rows total       97,444

colnames(dat)
#>  [1] "weight_g"  "length_cm" "year"      "lat"       "lon"       "quarter"  
#>  [7] "IDx"       "ices_rect" "sub_div"   "depth"     "id_oxy"    "oxy"      
#> [13] "id_temp"   "temp"

dat <- dat %>% dplyr::select(-id_temp, -id_oxy)

# Drop NA temp
dat <- dat %>% drop_na(temp)
#> drop_na: no rows removed

Add UTM coords

# First add UTM coords
# Add UTM coords
# Function
LongLatToUTM <- function(x, y, zone){
  xy <- data.frame(ID = 1:length(x), X = x, Y = y)
  coordinates(xy) <- c("X", "Y")
  proj4string(xy) <- CRS("+proj=longlat +datum=WGS84")  ## for example
  res <- spTransform(xy, CRS(paste("+proj=utm +zone=",zone," ellps=WGS84",sep='')))
  return(as.data.frame(res))
}

utm_coords <- LongLatToUTM(dat$lon, dat$lat, zone = 33)
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
#> prefer_proj): Discarded datum Unknown based on WGS84 ellipsoid in CRS definition
dat$X <- utm_coords$X/1000 # for computational reasons
dat$Y <- utm_coords$Y/1000 # for computational reasons

Add cod and flounder density covariates

# This is so that we can standardize the prediction grid with respect to the data
density <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/mdat_cpue_q_1_4.csv")

density <- density %>% filter(quarter == 4)

# Load models
mcod <- readRDS("output/mcod.rds")
mfle <- readRDS("output/mfle.rds")

# We need to scale data with respect to the mean and sd in the data to it was fitted to (cpue data)
density_mean_depth <- mean(density$depth)
density_sd_depth <- sd(density$depth)

# Scale depth in the data
dat <- dat %>%
  mutate(depth_sc = (depth - density_mean_depth) / density_sd_depth)

hist((density$depth - mean(density$depth)) / sd(density$depth))

hist(dat$depth_sc)


# Predict from the density models
cpue_cod <- exp(predict(mcod, newdata = dat)$est)
cpue_fle <- exp(predict(mfle, newdata = dat)$est)

dat$density_cod <- cpue_cod
dat$density_fle <- cpue_fle

# Inspect
ggplot(dat, aes(log(density_cod))) + geom_histogram()

ggplot(dat, aes(log(density_fle))) + geom_histogram()


# Remove the scaled depth variable
dat <- dat %>% dplyr::select(-depth_sc)

… And secondly by doing a left_join from the CPUE data using haul.id

d_cod_select <- density %>% dplyr::select(density, IDx) %>% rename("density_cod_data" = "density")
d_fle_select <- density %>% dplyr::select(density_fle, IDx) %>% rename("density_fle_data" = "density_fle")

dat <- left_join(dat, d_cod_select)
dat <- left_join(dat, d_fle_select)

# Check how well they correspond
ggplot(dat, aes(density_cod_data, density_cod)) + geom_point() + geom_abline(color = "red")
#> Warning: Removed 10669 rows containing missing values (geom_point).

ggplot(dat, aes(density_fle_data, density_fle)) + geom_point() + geom_abline(color = "red")
#> Warning: Removed 10669 rows containing missing values (geom_point).

Add Saduria from raster covariates

saduria <- raster("data/saduria_tif/FWBiomassm_raster_19812019presHighweightcor_no0_newZi.tif")
#> Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
#> prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition

saduria_longlat = projectRaster(saduria, crs = ('+proj=longlat'))

crs(saduria)
#> CRS arguments:
#>  +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80
#> +units=m +no_defs
extent(saduria)
#> class      : Extent 
#> xmin       : 4285000 
#> xmax       : 5440000 
#> ymin       : 3400000 
#> ymax       : 4810000

plot(saduria_longlat)


# Now extract the values from the saduria raster to the data
dat$density_saduria <- extract(saduria_longlat, dat[, 5:4])

ggplot(dat, aes(lon, lat, color = density_saduria)) +
  geom_point()

Add large scale variables from the prediciton grid

# First explore what the difference is between filtering depths and oxygen within their niche rather than the total in the rectangle

pred_grid1 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/pred_grid_(1_2).csv")
#> Rows: 129346 Columns: 29
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): ices_rect
#> dbl (28): X, Y, depth, year, oxy, temp, lon, lat, sub_div, density_saduria, ...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pred_grid2 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/pred_grid_(2_2).csv")
#> Rows: 120107 Columns: 29
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): ices_rect
#> dbl (28): X, Y, depth, year, oxy, temp, lon, lat, sub_div, density_saduria, ...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

pred_grid <- bind_rows(pred_grid1, pred_grid2)
 
# First create ID's for joining
dat <- dat %>% mutate(year_rect_id = paste(year, ices_rect, sep = "_"),
                      year_sd_id = paste(year, sub_div, sep = "_"))
#> mutate: new variable 'year_rect_id' (character) with 880 unique values and 0% NA
#>         new variable 'year_sd_id' (character) with 131 unique values and 0% NA

# t <- pred_grid %>% 
#   mutate(test_id = paste(year, ices_rect)) %>% 
#   group_by(test_id) %>% 
#   mutate(n = unique(temp_rec)) %>% 
#   ungroup() %>% 
#   distinct(n)
# 
# tt <- pred_grid %>% filter(year == "1994" & ices_rect == "39G7")
# unique(tt$temp_rec)

# Ok, some weird rounding error! Anyhow... 

# Take distinct year + ices_rect id's and select the variables
pred_grid_rec_vars <- pred_grid %>% 
  mutate(year_rect_id = paste(year, ices_rect, sep = "_")) %>% 
  distinct(year_rect_id, .keep_all = TRUE) %>% 
  dplyr::select(year_rect_id,
                density_saduria_rec, density_cod_rec, density_fle_rec,
                depth_rec, oxy_rec, temp_rec, biomass_spr, biomass_her)
#> mutate: new variable 'year_rect_id' (character) with 1,647 unique values and 0% NA
#> distinct: removed 247,806 rows (99%), 1,647 rows remaining
  
# Join!
dat <- left_join(dat, pred_grid_rec_vars)
#> Joining, by = "year_rect_id"
#> left_join: added 8 columns (density_saduria_rec, density_cod_rec, density_fle_rec, depth_rec, oxy_rec, …)
#> 
#>            > rows only in x      320
#> 
#>            > rows only in y  (   781)
#> 
#>            > matched rows     97,124
#> 
#>            >                 ========
#> 
#>            > rows total       97,444

# Take distinct year + sub_div id's and select the variables
pred_grid_sd_vars <- pred_grid %>% 
  mutate(year_sd_id = paste(year, sub_div, sep = "_")) %>% 
  distinct(year_sd_id, .keep_all = TRUE) %>% 
  dplyr::select(year_sd_id,
                density_saduria_sd, density_cod_sd, density_fle_sd,
                depth_sd, oxy_sd, temp_sd, biomass_spr_sd, biomass_her_sd)
#> mutate: new variable 'year_sd_id' (character) with 135 unique values and 0% NA
#> distinct: removed 249,318 rows (>99%), 135 rows remaining

# Join!
dat <- left_join(dat, pred_grid_sd_vars)
#> Joining, by = "year_sd_id"
#> left_join: added 8 columns (density_saduria_sd, density_cod_sd, density_fle_sd, depth_sd, oxy_sd, …)
#> 
#>            > rows only in x        0
#> 
#>            > rows only in y  (     4)
#> 
#>            > matched rows     97,444
#> 
#>            >                 ========
#> 
#>            > rows total       97,444

Save data

colnames(dat)
#>  [1] "weight_g"            "length_cm"           "year"               
#>  [4] "lat"                 "lon"                 "quarter"            
#>  [7] "IDx"                 "ices_rect"           "sub_div"            
#> [10] "depth"               "oxy"                 "temp"               
#> [13] "X"                   "Y"                   "density_cod"        
#> [16] "density_fle"         "density_cod_data"    "density_fle_data"   
#> [19] "density_saduria"     "year_rect_id"        "year_sd_id"         
#> [22] "density_saduria_rec" "density_cod_rec"     "density_fle_rec"    
#> [25] "depth_rec"           "oxy_rec"             "temp_rec"           
#> [28] "biomass_spr"         "biomass_her"         "density_saduria_sd" 
#> [31] "density_cod_sd"      "density_fle_sd"      "depth_sd"           
#> [34] "oxy_sd"              "temp_sd"             "biomass_spr_sd"     
#> [37] "biomass_her_sd"

# Check for outliers
dat <- dat %>% 
  mutate(Fulton_K = weight_g/(0.01*length_cm^3)) %>% 
  filter(Fulton_K < 3 & Fulton_K > 0.15) # Visual exploration, larger values likely data entry errors

# Trim data a little bit to not make it unnecessarily large
dat <- dat %>%
  dplyr::select(-IDx, -quarter, -IDx, -year_sd_id, -year_rect_id, -Fulton_K)

dat_93_06 <- dat %>% filter(year < 2007)
dat_07_19 <- dat %>% filter(year > 2006)

write.csv(dat_93_06, file = "data/for_analysis/mdat_cond_(1_2).csv", row.names = FALSE)
write.csv(dat_07_19, file = "data/for_analysis/mdat_cond_(2_2).csv", row.names = FALSE)
niche_rect <- pred_grid %>%
  filter(depth < 100 & oxy > 1.5) %>%
  group_by(ices_rect, year) %>%
  summarise(median_depth = median(depth),
            median_oxy = median(oxy)) %>%
  mutate(year_rect_id = paste(year, ices_rect, sep = "_"),
         type = "niche_data") %>%
  ungroup()
#> filter: removed 61,897 rows (25%), 187,556 rows remaining
#> group_by: 2 grouping variables (ices_rect, year)
#> summarise: now 1,620 rows and 4 columns, one group variable remaining (ices_rect)
#> mutate (grouped): new variable 'year_rect_id' (character) with 1,620 unique values and 0% NA
#>                   new variable 'type' (character) with one unique value and 0% NA
#> ungroup: no grouping variables

all_rect <- pred_grid %>%
  group_by(ices_rect, year) %>%
  summarise(median_depth = median(depth),
            median_oxy = median(oxy)) %>%
  mutate(year_rect_id = paste(year, ices_rect, sep = "_"),
         type = "all_data") %>%
  ungroup()
#> group_by: 2 grouping variables (ices_rect, year)
#> summarise: now 1,647 rows and 4 columns, one group variable remaining (ices_rect)
#> mutate (grouped): new variable 'year_rect_id' (character) with 1,647 unique values and 0% NA
#>                   new variable 'type' (character) with one unique value and 0% NA
#> ungroup: no grouping variables

all_dat <- bind_rows(all_rect, niche_rect)

# Now add in the condition data to calculate sample size per rectangle
# Filter
cond1 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/mdat_cond_(1_2).csv")
#> Rows: 39391 Columns: 25
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): ices_rect
#> dbl (24): weight_g, length_cm, year, lat, lon, sub_div, depth, oxy, temp, X,...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
cond2 <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/cod_condition/master/data/for_analysis/mdat_cond_(2_2).csv")
#> Rows: 58020 Columns: 25
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (1): ices_rect
#> dbl (24): weight_g, length_cm, year, lat, lon, sub_div, depth, oxy, temp, X,...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

cond <- bind_rows(cond1, cond2) %>% mutate(year_rect_id = paste(year, ices_rect, sep = "_"))
#> mutate: new variable 'year_rect_id' (character) with 880 unique values and 0% NA

# Join data for lm
all_rect2 <- all_rect %>% dplyr::select(year_rect_id, median_depth) %>%
  mutate(median_depth_sc = (median_depth - mean(median_depth))/sd(median_depth))
#> mutate: new variable 'median_depth_sc' (double) with 60 unique values and 0% NA

niche_rect2 <- niche_rect %>% dplyr::select(year_rect_id, median_depth) %>%
  mutate(median_depth_sc = (median_depth - mean(median_depth))/sd(median_depth))
#> mutate: new variable 'median_depth_sc' (double) with 295 unique values and 0% NA

cond_all <- left_join(cond, all_rect2)
#> Joining, by = "year_rect_id"
#> left_join: added 2 columns (median_depth, median_depth_sc)
#> 
#>            > rows only in x      320
#> 
#>            > rows only in y  (   781)
#> 
#>            > matched rows     97,091
#> 
#>            >                 ========
#> 
#>            > rows total       97,411
cond_niche <- left_join(cond, niche_rect2)
#> Joining, by = "year_rect_id"
#> left_join: added 2 columns (median_depth, median_depth_sc)
#> 
#>            > rows only in x      320
#> 
#>            > rows only in y  (   754)
#> 
#>            > matched rows     97,091
#> 
#>            >                 ========
#> 
#>            > rows total       97,411

summary(lm(log(weight_g) ~ log(length_cm) + median_depth_sc, data = cond_all))
#> 
#> Call:
#> lm(formula = log(weight_g) ~ log(length_cm) + median_depth_sc, 
#>     data = cond_all)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -1.56291 -0.07639  0.00456  0.08272  1.15374 
#> 
#> Coefficients:
#>                   Estimate Std. Error  t value Pr(>|t|)    
#> (Intercept)     -4.6403346  0.0034142 -1359.15   <2e-16 ***
#> log(length_cm)   2.9833335  0.0009767  3054.44   <2e-16 ***
#> median_depth_sc -0.0352010  0.0006678   -52.71   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.1368 on 97088 degrees of freedom
#>   (320 observations deleted due to missingness)
#> Multiple R-squared:  0.9897, Adjusted R-squared:  0.9897 
#> F-statistic: 4.673e+06 on 2 and 97088 DF,  p-value: < 2.2e-16
summary(lm(log(weight_g) ~ log(length_cm) + median_depth_sc, data = cond_niche))
#> 
#> Call:
#> lm(formula = log(weight_g) ~ log(length_cm) + median_depth_sc, 
#>     data = cond_niche)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -1.56612 -0.07526  0.00525  0.08195  1.18544 
#> 
#> Coefficients:
#>                   Estimate Std. Error  t value Pr(>|t|)    
#> (Intercept)     -4.6423652  0.0033843 -1371.75   <2e-16 ***
#> log(length_cm)   2.9866612  0.0009716  3074.12   <2e-16 ***
#> median_depth_sc -0.0340829  0.0005098   -66.86   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 0.1357 on 97088 degrees of freedom
#>   (320 observations deleted due to missingness)
#> Multiple R-squared:  0.9899, Adjusted R-squared:  0.9899 
#> F-statistic: 4.753e+06 on 2 and 97088 DF,  p-value: < 2.2e-16

t <- cond_all %>% filter(median_depth < 100)
#> filter: removed 2,904 rows (3%), 94,507 rows remaining

plot(cond_all$median_depth_sc)

plot(cond_niche$median_depth_sc)


cond2 <- cond %>%
  group_by(year, ices_rect) %>%
  summarise(n = n()) %>%
  mutate(year_rect_id = paste(year, ices_rect, sep = "_")) %>%
  ungroup() %>%
  dplyr::select(-year, -ices_rect)
#> group_by: 2 grouping variables (year, ices_rect)
#> summarise: now 880 rows and 3 columns, one group variable remaining (year)
#> mutate (grouped): new variable 'year_rect_id' (character) with 880 unique values and 0% NA
#> ungroup: no grouping variables

cond2 %>% arrange(n) %>% as.data.frame()
#>       n year_rect_id
#> 1     1    1993_39G5
#> 2     1    1994_40G8
#> 3     1    1994_44G8
#> 4     1    1997_40G8
#> 5     1    1999_43G9
#> 6     1    2002_37G4
#> 7     1    2003_41G6
#> 8     1    2003_42G6
#> 9     1    2003_43G6
#> 10    1    2004_43G8
#> 11    1    2004_43H0
#> 12    1    2006_39G2
#> 13    1    2007_45G9
#> 14    1    2008_37G3
#> 15    1    2009_43H1
#> 16    1    2009_44H0
#> 17    1    2010_37G6
#> 18    1    2011_40G8
#> 19    1    2011_43G6
#> 20    1    2012_37G9
#> 21    1    2012_45G9
#> 22    1    2014_37G6
#> 23    1    2014_37G8
#> 24    1    2014_39G9
#> 25    1    2016_42H0
#> 26    1    2017_40G8
#> 27    1    2017_42G6
#> 28    1    2018_38G7
#> 29    1    2018_40G7
#> 30    1    2018_45H1
#> 31    1    2019_43G7
#> 32    2    1994_41G7
#> 33    2    1996_39G5
#> 34    2    1997_42G7
#> 35    2    1999_42G7
#> 36    2    2001_45G9
#> 37    2    2004_40G9
#> 38    2    2005_37G3
#> 39    2    2008_38G6
#> 40    2    2008_43G9
#> 41    2    2010_41G8
#> 42    2    2014_41G6
#> 43    2    2015_37G8
#> 44    2    2016_42G7
#> 45    2    2017_37G6
#> 46    2    2018_37G4
#> 47    2    2019_44G9
#> 48    3    2005_37G4
#> 49    3    2005_38G7
#> 50    3    2005_43G9
#> 51    3    2010_37G5
#> 52    3    2012_43G6
#> 53    3    2015_39G9
#> 54    3    2015_44H0
#> 55    3    2017_38G7
#> 56    3    2018_38G9
#> 57    3    2019_42G6
#> 58    4    1993_37G4
#> 59    4    1995_38G4
#> 60    4    1998_42G7
#> 61    4    1998_44H1
#> 62    4    2000_38G6
#> 63    4    2001_42G7
#> 64    4    2002_43G6
#> 65    4    2003_45G9
#> 66    4    2006_44G9
#> 67    4    2009_37G4
#> 68    4    2012_41H0
#> 69    4    2013_38G2
#> 70    4    2014_39G2
#> 71    4    2018_37G6
#> 72    5    1997_37G4
#> 73    5    2003_37G6
#> 74    5    2007_45H1
#> 75    5    2009_42G7
#> 76    5    2011_39G8
#> 77    5    2012_37G6
#> 78    5    2012_38G7
#> 79    5    2014_43G8
#> 80    5    2017_41H0
#> 81    6    1999_44G9
#> 82    6    2002_37G5
#> 83    6    2002_38G6
#> 84    6    2005_41G9
#> 85    6    2007_42G7
#> 86    6    2011_39G2
#> 87    6    2011_42G7
#> 88    6    2014_37G2
#> 89    6    2014_42G7
#> 90    6    2015_37G9
#> 91    6    2018_41H0
#> 92    7    1997_43G6
#> 93    7    2003_38G8
#> 94    7    2004_37G3
#> 95    7    2005_43G6
#> 96    7    2007_37G4
#> 97    7    2008_37G4
#> 98    7    2011_39G5
#> 99    7    2011_43G7
#> 100   7    2013_37G6
#> 101   7    2016_43G8
#> 102   8    1993_43G9
#> 103   8    1997_39G6
#> 104   8    1998_41G7
#> 105   8    1998_43H1
#> 106   8    2009_39G5
#> 107   8    2012_43G8
#> 108   8    2014_38G7
#> 109   8    2015_38G9
#> 110   8    2015_39G5
#> 111   8    2016_43H0
#> 112   8    2017_39G6
#> 113   8    2019_44H0
#> 114   8    2019_44H1
#> 115   9    2000_43G6
#> 116   9    2002_37G2
#> 117   9    2003_41G9
#> 118   9    2003_43G9
#> 119   9    2003_43H0
#> 120   9    2011_43G8
#> 121   9    2012_42G7
#> 122   9    2017_38G2
#> 123  10    1997_43G9
#> 124  10    1997_44G9
#> 125  10    1999_43G6
#> 126  10    2009_40H0
#> 127  10    2009_43G6
#> 128  10    2010_44H0
#> 129  10    2017_43G8
#> 130  11    1994_37G3
#> 131  11    1997_41G8
#> 132  11    2000_38G2
#> 133  11    2000_45H1
#> 134  11    2002_43H1
#> 135  11    2003_37G5
#> 136  11    2005_37G5
#> 137  11    2012_42H0
#> 138  11    2013_43G9
#> 139  11    2018_42G6
#> 140  11    2019_43G6
#> 141  12    1993_41G7
#> 142  12    1995_40G6
#> 143  12    2000_40G9
#> 144  12    2004_39G9
#> 145  12    2005_41G6
#> 146  12    2007_37G8
#> 147  12    2008_38G7
#> 148  12    2013_37G2
#> 149  12    2015_43G6
#> 150  12    2017_37G9
#> 151  12    2019_42G7
#> 152  13    2000_44G9
#> 153  13    2006_38G8
#> 154  13    2009_38G7
#> 155  13    2010_43G6
#> 156  13    2013_43G8
#> 157  13    2015_37G2
#> 158  14    2000_39G6
#> 159  14    2000_43G9
#> 160  14    2006_37G2
#> 161  14    2006_37G4
#> 162  14    2013_39G2
#> 163  14    2016_43G7
#> 164  15    2011_37G2
#> 165  15    2012_37G3
#> 166  15    2012_43H0
#> 167  15    2015_44G9
#> 168  15    2016_37G2
#> 169  15    2017_43G7
#> 170  15    2018_39G9
#> 171  15    2019_37G4
#> 172  15    2019_39G8
#> 173  16    2004_39G4
#> 174  16    2009_39G8
#> 175  16    2016_38G7
#> 176  17    1995_44G9
#> 177  17    2001_43H1
#> 178  17    2004_37G4
#> 179  17    2005_44H1
#> 180  17    2009_43G9
#> 181  17    2011_45G9
#> 182  17    2016_44G9
#> 183  18    1993_44G9
#> 184  18    1994_44G9
#> 185  18    1998_43G7
#> 186  18    1998_44G9
#> 187  18    1999_43H0
#> 188  18    2003_43G8
#> 189  18    2006_43G6
#> 190  18    2009_37G6
#> 191  18    2011_37G6
#> 192  18    2011_38G7
#> 193  18    2011_43G9
#> 194  18    2017_43G9
#> 195  18    2018_43G9
#> 196  18    2019_40G8
#> 197  19    1998_38G4
#> 198  19    2004_38G9
#> 199  19    2007_41G8
#> 200  19    2016_37G9
#> 201  20    2003_38G5
#> 202  20    2008_39G2
#> 203  20    2015_44H1
#> 204  20    2018_42G7
#> 205  20    2019_41G8
#> 206  21    2007_43G7
#> 207  21    2013_40G8
#> 208  21    2016_39G8
#> 209  22    2002_40G6
#> 210  22    2002_43H0
#> 211  22    2005_41G7
#> 212  22    2007_42G6
#> 213  22    2015_38G7
#> 214  23    2000_44H1
#> 215  23    2003_37G9
#> 216  23    2005_45G9
#> 217  23    2010_38G8
#> 218  23    2011_41G8
#> 219  23    2011_42G6
#> 220  23    2014_37G4
#> 221  23    2016_37G3
#> 222  23    2016_39G7
#> 223  23    2017_37G2
#> 224  23    2019_38G7
#> 225  24    2000_41H0
#> 226  24    2002_38G7
#> 227  24    2010_37G2
#> 228  24    2011_44G9
#> 229  24    2015_38G2
#> 230  24    2018_41G7
#> 231  25    1999_39G5
#> 232  25    2001_42G6
#> 233  25    2004_40G7
#> 234  25    2011_41G9
#> 235  25    2019_37G2
#> 236  26    2003_43H1
#> 237  26    2008_37G9
#> 238  26    2012_39G4
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#> 873 604    2010_39G9
#> 874 612    2008_40H0
#> 875 629    2008_39H0
#> 876 639    2016_40G5
#> 877 763    2016_38G4
#> 878 778    2005_40H0
#> 879 798    2004_38G5
#> 880 836    2006_39H0

all_dat <- left_join(all_dat, cond2)
#> Joining, by = "year_rect_id"
#> left_join: added one column (n)
#> 
#>            > rows only in x   1,535
#> 
#>            > rows only in y  (   14)
#> 
#>            > matched rows     1,732
#> 
#>            >                 =======
#> 
#>            > rows total       3,267

hist(all_dat$n)


all_dat %>% arrange(n) %>% as.data.frame()
#>      ices_rect year median_depth  median_oxy year_rect_id       type   n
#> 1         37G3 2008     15.00000  7.04252318    2008_37G3   all_data   1
#> 2         37G4 2002     13.00000  7.55310374    2002_37G4   all_data   1
#> 3         37G6 2010     18.00000  7.94124371    2010_37G6   all_data   1
#> 4         37G6 2014     18.00000  7.32908176    2014_37G6   all_data   1
#> 5         37G8 2014     20.86536  6.64736446    2014_37G8   all_data   1
#> 6         37G9 2012     43.07566  7.02251739    2012_37G9   all_data   1
#> 7         38G7 2018     21.00000  7.40310822    2018_38G7   all_data   1
#> 8         39G2 2006     19.00000  6.77359451    2006_39G2   all_data   1
#> 9         39G5 1993     85.00000  1.55825941    1993_39G5   all_data   1
#> 10        39G9 2014     99.00000  3.13034042    2014_39G9   all_data   1
#> 11        40G7 2018     38.00000  7.61379529    2018_40G7   all_data   1
#> 12        40G8 1994     97.98204  3.32860756    1994_40G8   all_data   1
#> 13        40G8 1997     97.98204  2.94276873    1997_40G8   all_data   1
#> 14        40G8 2011     97.98204  1.84379946    2011_40G8   all_data   1
#> 15        40G8 2017     97.98204  2.68929285    2017_40G8   all_data   1
#> 16        41G6 2003     32.27464  7.62731978    2003_41G6   all_data   1
#> 17        42G6 2003     29.09908  7.58598890    2003_42G6   all_data   1
#> 18        42G6 2017     29.09908  7.27095227    2017_42G6   all_data   1
#> 19        42H0 2016     57.59084  7.56993608    2016_42H0   all_data   1
#> 20        43G6 2003     30.91015  7.30400321    2003_43G6   all_data   1
#> 21        43G6 2011     30.91015  4.92048295    2011_43G6   all_data   1
#> 22        43G7 2019     83.80202  3.75597524    2019_43G7   all_data   1
#> 23        43G8 2004     27.86322  7.91112818    2004_43G8   all_data   1
#> 24        43G9 1999    180.66532  0.12117647    1999_43G9   all_data   1
#> 25        43H0 2004    124.04904  2.36574769    2004_43H0   all_data   1
#> 26        43H1 2009     23.00000  7.56536304    2009_43H1   all_data   1
#> 27        44G8 1994    110.76247  2.65520274    1994_44G8   all_data   1
#> 28        44H0 2009    139.00000 -0.27921338    2009_44H0   all_data   1
#> 29        37G3 2008     15.00000  7.04252318    2008_37G3 niche_data   1
#> 30        37G4 2002     13.00000  7.55310374    2002_37G4 niche_data   1
#> 31        37G6 2010     18.00000  7.94124371    2010_37G6 niche_data   1
#> 32        37G6 2014     18.00000  7.32908176    2014_37G6 niche_data   1
#> 33        37G8 2014     20.86536  6.64736446    2014_37G8 niche_data   1
#> 34        37G9 2012     43.07566  7.02251739    2012_37G9 niche_data   1
#> 35        38G7 2018     21.00000  7.40310822    2018_38G7 niche_data   1
#> 36        39G2 2006     19.00000  6.77359451    2006_39G2 niche_data   1
#> 37        39G5 1993     79.00000  2.05975759    1993_39G5 niche_data   1
#> 38        39G9 2014     86.87663  4.43659578    2014_39G9 niche_data   1
#> 39        40G7 2018     38.00000  7.61379529    2018_40G7 niche_data   1
#> 40        40G8 1994     90.00000  4.27560823    1994_40G8 niche_data   1
#> 41        40G8 1997     90.00000  3.87455890    1997_40G8 niche_data   1
#> 42        40G8 2011     89.56757  2.90402990    2011_40G8 niche_data   1
#> 43        40G8 2017     90.00000  3.10645348    2017_40G8 niche_data   1
#> 44        41G6 2003     32.27464  7.62731978    2003_41G6 niche_data   1
#> 45        42G6 2003     27.40247  7.59361411    2003_42G6 niche_data   1
#> 46        42G6 2017     28.72478  7.28307473    2017_42G6 niche_data   1
#> 47        42H0 2016     43.82713  7.89440398    2016_42H0 niche_data   1
#> 48        43G6 2003     30.91015  7.30400321    2003_43G6 niche_data   1
#> 49        43G6 2011     30.91015  4.92048295    2011_43G6 niche_data   1
#> 50        43G7 2019     60.79264  6.10456440    2019_43G7 niche_data   1
#> 51        43G8 2004     27.22653  7.96317811    2004_43G8 niche_data   1
#> 52        43G9 1999     64.11560  5.23046770    1999_43G9 niche_data   1
#> 53        43H0 2004     52.76306  7.46517460    2004_43H0 niche_data   1
#> 54        43H1 2009     23.00000  7.56536304    2009_43H1 niche_data   1
#> 55        44G8 1994     27.80362  7.94615985    1994_44G8 niche_data   1
#> 56        44H0 2009     55.04318  6.41729513    2009_44H0 niche_data   1
#> 57        37G3 2005     15.00000  6.90368132    2005_37G3   all_data   2
#> 58        37G4 2018     13.00000  7.66608766    2018_37G4   all_data   2
#> 59        37G6 2017     18.00000  7.60525041    2017_37G6   all_data   2
#> 60        37G8 2015     20.86536  7.02239861    2015_37G8   all_data   2
#> 61        38G6 2008     26.52553  7.51053972    2008_38G6   all_data   2
#> 62        39G5 1996     85.00000  0.78096472    1996_39G5   all_data   2
#> 63        40G9 2004     83.07490  3.76914662    2004_40G9   all_data   2
#> 64        41G6 2014     32.27464  6.60575707    2014_41G6   all_data   2
#> 65        41G7 1994     38.00000  7.80753057    1994_41G7   all_data   2
#> 66        41G8 2010     96.71104  1.58156640    2010_41G8   all_data   2
#> 67        42G7 1997     79.04017  2.64258821    1997_42G7   all_data   2
#> 68        42G7 1999     79.04017  2.25255568    1999_42G7   all_data   2
#> 69        42G7 2016     79.04017  2.38158067    2016_42G7   all_data   2
#> 70        43G9 2008    180.66532 -0.74241444    2008_43G9   all_data   2
#> 71        44G9 2019     98.95766  1.83687666    2019_44G9   all_data   2
#> 72        37G3 2005     15.00000  6.90368132    2005_37G3 niche_data   2
#> 73        37G4 2018     13.00000  7.66608766    2018_37G4 niche_data   2
#> 74        37G6 2017     18.00000  7.60525041    2017_37G6 niche_data   2
#> 75        37G8 2015     20.86536  7.02239861    2015_37G8 niche_data   2
#> 76        38G6 2008     26.43109  7.51095449    2008_38G6 niche_data   2
#> 77        39G5 1996     76.00000  2.58868678    1996_39G5 niche_data   2
#> 78        40G9 2004     82.00000  3.88205815    2004_40G9 niche_data   2
#> 79        41G6 2014     32.27464  6.60575707    2014_41G6 niche_data   2
#> 80        41G7 1994     38.00000  7.80753057    1994_41G7 niche_data   2
#> 81        41G8 2010     38.75330  6.69639869    2010_41G8 niche_data   2
#> 82        42G7 1997     64.91632  4.78624119    1997_42G7 niche_data   2
#> 83        42G7 1999     65.76437  3.37135997    1999_42G7 niche_data   2
#> 84        42G7 2016     61.97936  4.53726700    2016_42G7 niche_data   2
#> 85        43G9 2008     65.81358  4.56399125    2008_43G9 niche_data   2
#> 86        44G9 2019     46.28079  6.10583140    2019_44G9 niche_data   2
#> 87        37G4 2005     13.00000  7.32508624    2005_37G4   all_data   3
#> 88        37G5 2010     20.00000  7.92823521    2010_37G5   all_data   3
#> 89        38G7 2005     21.00000  7.61807145    2005_38G7   all_data   3
#> 90        38G7 2017     21.00000  7.55112627    2017_38G7   all_data   3
#> 91        38G9 2018     91.00641  3.29989341    2018_38G9   all_data   3
#> 92        39G9 2015     99.00000  2.35879825    2015_39G9   all_data   3
#> 93        42G6 2019     29.09908  7.61255562    2019_42G6   all_data   3
#> 94        43G6 2012     30.91015  7.09055911    2012_43G6   all_data   3
#> 95        43G9 2005    180.66532  0.37801714    2005_43G9   all_data   3
#> 96        44H0 2015    139.00000  3.09611457    2015_44H0   all_data   3
#> 97        37G4 2005     13.00000  7.32508624    2005_37G4 niche_data   3
#> 98        37G5 2010     20.00000  7.92823521    2010_37G5 niche_data   3
#> 99        38G7 2005     21.00000  7.61807145    2005_38G7 niche_data   3
#> 100       38G7 2017     21.00000  7.55112627    2017_38G7 niche_data   3
#> 101       38G9 2018     61.59609  4.66372831    2018_38G9 niche_data   3
#> 102       39G9 2015     86.00000  4.45895504    2015_39G9 niche_data   3
#> 103       42G6 2019     29.09908  7.61255562    2019_42G6 niche_data   3
#> 104       43G6 2012     30.91015  7.09055911    2012_43G6 niche_data   3
#> 105       43G9 2005     66.08981  5.05341596    2005_43G9 niche_data   3
#> 106       44H0 2015     56.07331  6.75448471    2015_44H0 niche_data   3
#> 107       37G4 1993     13.00000  7.78946155    1993_37G4   all_data   4
#> 108       37G4 2009     13.00000  7.59509036    2009_37G4   all_data   4
#> 109       37G6 2018     18.00000  7.30501805    2018_37G6   all_data   4
#> 110       38G2 2013     17.00000  7.06535335    2013_38G2   all_data   4
#> 111       38G4 1995     25.69453  7.19164735    1995_38G4   all_data   4
#> 112       38G6 2000     26.52553  7.15525827    2000_38G6   all_data   4
#> 113       39G2 2014     19.00000  6.94372458    2014_39G2   all_data   4
#> 114       41H0 2012     28.04549  7.43970451    2012_41H0   all_data   4
#> 115       42G7 1998     79.04017  2.52257872    1998_42G7   all_data   4
#> 116       42G7 2001     79.04017  2.29870363    2001_42G7   all_data   4
#> 117       43G6 2002     30.91015  7.75129513    2002_43G6   all_data   4
#> 118       44G9 2006     98.95766  1.36538198    2006_44G9   all_data   4
#> 119       44H1 1998     30.23161  7.56486055    1998_44H1   all_data   4
#> 120       37G4 1993     13.00000  7.78946155    1993_37G4 niche_data   4
#> 121       37G4 2009     13.00000  7.59509036    2009_37G4 niche_data   4
#> 122       37G6 2018     18.00000  7.30501805    2018_37G6 niche_data   4
#> 123       38G2 2013     17.00000  7.06535335    2013_38G2 niche_data   4
#> 124       38G4 1995     25.69453  7.19164735    1995_38G4 niche_data   4
#> 125       38G6 2000     26.33665  7.15560073    2000_38G6 niche_data   4
#> 126       39G2 2014     19.00000  6.94372458    2014_39G2 niche_data   4
#> 127       41H0 2012     28.00000  7.44015718    2012_41H0 niche_data   4
#> 128       42G7 1998     67.01072  3.36734223    1998_42G7 niche_data   4
#> 129       42G7 2001     65.25833  3.61106619    2001_42G7 niche_data   4
#> 130       43G6 2002     30.91015  7.75129513    2002_43G6 niche_data   4
#> 131       44G9 2006     44.74013  6.12382209    2006_44G9 niche_data   4
#> 132       44H1 1998     30.23161  7.56486055    1998_44H1 niche_data   4
#> 133       37G4 1997     13.00000  7.45470591    1997_37G4   all_data   5
#> 134       37G6 2003     18.00000  7.21182562    2003_37G6   all_data   5
#> 135       37G6 2012     18.00000  7.48769689    2012_37G6   all_data   5
#> 136       38G7 2012     21.00000  7.50098775    2012_38G7   all_data   5
#> 137       39G8 2011     90.00000  3.11228662    2011_39G8   all_data   5
#> 138       41H0 2017     28.04549  7.45998708    2017_41H0   all_data   5
#> 139       42G7 2009     79.04017  2.63385718    2009_42G7   all_data   5
#> 140       43G8 2014     27.86322  7.58469251    2014_43G8   all_data   5
#> 141       37G4 1997     13.00000  7.45470591    1997_37G4 niche_data   5
#> 142       37G6 2003     18.00000  7.21182562    2003_37G6 niche_data   5
#> 143       37G6 2012     18.00000  7.48769689    2012_37G6 niche_data   5
#> 144       38G7 2012     21.00000  7.50098775    2012_38G7 niche_data   5
#> 145       39G8 2011     88.00000  3.23652997    2011_39G8 niche_data   5
#> 146       41H0 2017     28.00000  7.46102673    2017_41H0 niche_data   5
#> 147       42G7 2009     65.36777  4.03001164    2009_42G7 niche_data   5
#> 148       43G8 2014     27.22653  7.62000003    2014_43G8 niche_data   5
#> 149       37G2 2014     14.28574  5.61331322    2014_37G2   all_data   6
#> 150       37G5 2002     20.00000  7.44120566    2002_37G5   all_data   6
#> 151       37G9 2015     43.07566  7.06321534    2015_37G9   all_data   6
#> 152       38G6 2002     26.52553  7.40953531    2002_38G6   all_data   6
#> 153       39G2 2011     19.00000  7.20898773    2011_39G2   all_data   6
#> 154       41G9 2005    123.90523  1.78608157    2005_41G9   all_data   6
#> 155       41H0 2018     28.04549  7.37741111    2018_41H0   all_data   6
#> 156       42G7 2007     79.04017  2.45316907    2007_42G7   all_data   6
#> 157       42G7 2011     79.04017  0.37349979    2011_42G7   all_data   6
#> 158       42G7 2014     79.04017  1.09904744    2014_42G7   all_data   6
#> 159       44G9 1999     98.95766  1.77511402    1999_44G9   all_data   6
#> 160       37G2 2014     14.28574  5.61331322    2014_37G2 niche_data   6
#> 161       37G5 2002     20.00000  7.44120566    2002_37G5 niche_data   6
#> 162       37G9 2015     43.07566  7.06321534    2015_37G9 niche_data   6
#> 163       38G6 2002     26.52553  7.40953531    2002_38G6 niche_data   6
#> 164       39G2 2011     19.00000  7.20898773    2011_39G2 niche_data   6
#> 165       41G9 2005     59.00000  6.97034217    2005_41G9 niche_data   6
#> 166       41H0 2018     28.00000  7.38099452    2018_41H0 niche_data   6
#> 167       42G7 2007     65.36777  4.04455804    2007_42G7 niche_data   6
#> 168       42G7 2011     54.03358  2.47096465    2011_42G7 niche_data   6
#> 169       42G7 2014     58.00000  3.66195286    2014_42G7 niche_data   6
#> 170       44G9 1999     46.02985  7.04389316    1999_44G9 niche_data   6
#> 171       37G3 2004     15.00000  7.30111186    2004_37G3   all_data   7
#> 172       37G4 2007     13.00000  7.49032100    2007_37G4   all_data   7
#> 173       37G4 2008     13.00000  7.48465903    2008_37G4   all_data   7
#> 174       37G6 2013     18.00000  7.74780479    2013_37G6   all_data   7
#> 175       38G8 2003     40.19787  6.64892395    2003_38G8   all_data   7
#> 176       39G5 2011     85.00000  0.23749470    2011_39G5   all_data   7
#> 177       43G6 1997     30.91015  7.35135863    1997_43G6   all_data   7
#> 178       43G6 2005     30.91015  7.89493588    2005_43G6   all_data   7
#> 179       43G7 2011     83.80202  0.85520905    2011_43G7   all_data   7
#> 180       43G8 2016     27.86322  7.52390019    2016_43G8   all_data   7
#> 181       37G3 2004     15.00000  7.30111186    2004_37G3 niche_data   7
#> 182       37G4 2007     13.00000  7.49032100    2007_37G4 niche_data   7
#> 183       37G4 2008     13.00000  7.48465903    2008_37G4 niche_data   7
#> 184       37G6 2013     18.00000  7.74780479    2013_37G6 niche_data   7
#> 185       38G8 2003     28.72128  6.89123088    2003_38G8 niche_data   7
#> 186       39G5 2011     74.94323  2.17253089    2011_39G5 niche_data   7
#> 187       43G6 1997     30.91015  7.35135863    1997_43G6 niche_data   7
#> 188       43G6 2005     30.91015  7.89493588    2005_43G6 niche_data   7
#> 189       43G7 2011     50.30114  4.12508152    2011_43G7 niche_data   7
#> 190       43G8 2016     27.20216  7.58500008    2016_43G8 niche_data   7
#> 191       38G7 2014     21.00000  7.43676112    2014_38G7   all_data   8
#> 192       38G9 2015     91.00641  5.66214357    2015_38G9   all_data   8
#> 193       39G5 2009     85.00000  0.91125274    2009_39G5   all_data   8
#> 194       39G5 2015     85.00000  0.67541813    2015_39G5   all_data   8
#> 195       39G6 1997     68.00000  3.32201361    1997_39G6   all_data   8
#> 196       39G6 2017     68.00000  5.35599303    2017_39G6   all_data   8
#> 197       41G7 1998     38.00000  6.79024933    1998_41G7   all_data   8
#> 198       43G8 2012     27.86322  7.44915406    2012_43G8   all_data   8
#> 199       43G9 1993    180.66532  0.41431188    1993_43G9   all_data   8
#> 200       43H0 2016    124.04904  5.69858938    2016_43H0   all_data   8
#> 201       43H1 1998     23.00000  7.56549243    1998_43H1   all_data   8
#> 202       44H0 2019    139.00000  1.15628240    2019_44H0   all_data   8
#> 203       44H1 2019     30.23161  7.43313598    2019_44H1   all_data   8
#> 204       38G7 2014     21.00000  7.43676112    2014_38G7 niche_data   8
#> 205       38G9 2015     61.59609  6.66540116    2015_38G9 niche_data   8
#> 206       39G5 2009     76.03769  2.96171929    2009_39G5 niche_data   8
#> 207       39G5 2015     70.00000  3.18983612    2015_39G5 niche_data   8
#> 208       39G6 1997     62.00000  4.40403757    1997_39G6 niche_data   8
#> 209       39G6 2017     62.38776  5.64092832    2017_39G6 niche_data   8
#> 210       41G7 1998     38.00000  6.79024933    1998_41G7 niche_data   8
#> 211       43G8 2012     27.22653  7.50257249    2012_43G8 niche_data   8
#> 212       43G9 1993     66.36603  7.23746640    1993_43G9 niche_data   8
#> 213       43H0 2016     52.76306  7.78000920    2016_43H0 niche_data   8
#> 214       43H1 1998     23.00000  7.56549243    1998_43H1 niche_data   8
#> 215       44H0 2019     56.07331  6.52727322    2019_44H0 niche_data   8
#> 216       44H1 2019     30.23161  7.43313598    2019_44H1 niche_data   8
#> 217       37G2 2002     14.28574  5.77079659    2002_37G2   all_data   9
#> 218       38G2 2017     17.00000  7.17032221    2017_38G2   all_data   9
#> 219       41G9 2003    123.90523  3.49113078    2003_41G9   all_data   9
#> 220       42G7 2012     79.04017  2.53298744    2012_42G7   all_data   9
#> 221       43G6 2000     30.91015  7.04204818    2000_43G6   all_data   9
#> 222       43G8 2011     27.86322  7.78958846    2011_43G8   all_data   9
#> 223       43G9 2003    180.66532  0.46426956    2003_43G9   all_data   9
#> 224       43H0 2003    124.04904  2.85135057    2003_43H0   all_data   9
#> 225       37G2 2002     14.28574  5.77079659    2002_37G2 niche_data   9
#> 226       38G2 2017     17.00000  7.17032221    2017_38G2 niche_data   9
#> 227       41G9 2003     59.00000  5.41335642    2003_41G9 niche_data   9
#> 228       42G7 2012     65.36777  3.84977683    2012_42G7 niche_data   9
#> 229       43G6 2000     30.91015  7.04204818    2000_43G6 niche_data   9
#> 230       43G8 2011     27.20216  7.84181386    2011_43G8 niche_data   9
#> 231       43G9 2003     66.36603  5.20664093    2003_43G9 niche_data   9
#> 232       43H0 2003     52.76306  7.25111239    2003_43H0 niche_data   9
#> 233       40H0 2009     44.00000  7.34696900    2009_40H0   all_data  10
#> 234       43G6 1999     30.91015  6.92468117    1999_43G6   all_data  10
#> 235       43G6 2009     30.91015  7.52318990    2009_43G6   all_data  10
#> 236       43G8 2017     27.86322  7.36671302    2017_43G8   all_data  10
#> 237       43G9 1997    180.66532  0.39483630    1997_43G9   all_data  10
#> 238       44G9 1997     98.95766  2.67299654    1997_44G9   all_data  10
#> 239       44H0 2010    139.00000  0.20439390    2010_44H0   all_data  10
#> 240       40H0 2009     44.00000  7.34696900    2009_40H0 niche_data  10
#> 241       43G6 1999     30.91015  6.92468117    1999_43G6 niche_data  10
#> 242       43G6 2009     30.91015  7.52318990    2009_43G6 niche_data  10
#> 243       43G8 2017     27.10108  7.47425298    2017_43G8 niche_data  10
#> 244       43G9 1997     66.36603  5.60227451    1997_43G9 niche_data  10
#> 245       44G9 1997     46.52287  7.09741269    1997_44G9 niche_data  10
#> 246       44H0 2010     56.07331  6.09822547    2010_44H0 niche_data  10
#> 247       37G3 1994     15.00000  7.35490158    1994_37G3   all_data  11
#> 248       37G5 2003     20.00000  7.15274469    2003_37G5   all_data  11
#> 249       37G5 2005     20.00000  7.40121269    2005_37G5   all_data  11
#> 250       38G2 2000     17.00000  6.58027470    2000_38G2   all_data  11
#> 251       41G8 1997     96.71104  2.72480810    1997_41G8   all_data  11
#> 252       42G6 2018     29.09908  7.74014289    2018_42G6   all_data  11
#> 253       42H0 2012     57.59084  7.38167228    2012_42H0   all_data  11
#> 254       43G6 2019     30.91015  7.32178380    2019_43G6   all_data  11
#> 255       43G9 2013    180.66532 -1.48522722    2013_43G9   all_data  11
#> 256       43H1 2002     23.00000  8.03063170    2002_43H1   all_data  11
#> 257       37G3 1994     15.00000  7.35490158    1994_37G3 niche_data  11
#> 258       37G5 2003     20.00000  7.15274469    2003_37G5 niche_data  11
#> 259       37G5 2005     20.00000  7.40121269    2005_37G5 niche_data  11
#> 260       38G2 2000     17.00000  6.58027470    2000_38G2 niche_data  11
#> 261       41G8 1997     39.05557  7.71045980    1997_41G8 niche_data  11
#> 262       42G6 2018     29.09908  7.74014289    2018_42G6 niche_data  11
#> 263       42H0 2012     43.63243  7.46811042    2012_42H0 niche_data  11
#> 264       43G6 2019     30.91015  7.32178380    2019_43G6 niche_data  11
#> 265       43G9 2013     61.90562  5.39571354    2013_43G9 niche_data  11
#> 266       43H1 2002     23.00000  8.03063170    2002_43H1 niche_data  11
#> 267       37G2 2013     14.28574  6.81300928    2013_37G2   all_data  12
#> 268       37G8 2007     20.86536  6.53866408    2007_37G8   all_data  12
#> 269       37G9 2017     43.07566  7.22649001    2017_37G9   all_data  12
#> 270       38G7 2008     21.00000  7.57822498    2008_38G7   all_data  12
#> 271       39G9 2004     99.00000  2.48357097    2004_39G9   all_data  12
#> 272       40G6 1995     50.04572  6.37610660    1995_40G6   all_data  12
#> 273       40G9 2000     83.07490  3.17226704    2000_40G9   all_data  12
#> 274       41G6 2005     32.27464  8.00770130    2005_41G6   all_data  12
#> 275       41G7 1993     38.00000  7.64772485    1993_41G7   all_data  12
#> 276       42G7 2019     79.04017  4.83801351    2019_42G7   all_data  12
#> 277       43G6 2015     30.91015  6.59965677    2015_43G6   all_data  12
#> 278       37G2 2013     14.28574  6.81300928    2013_37G2 niche_data  12
#> 279       37G8 2007     20.86536  6.53866408    2007_37G8 niche_data  12
#> 280       37G9 2017     43.07566  7.22649001    2017_37G9 niche_data  12
#> 281       38G7 2008     21.00000  7.57822498    2008_38G7 niche_data  12
#> 282       39G9 2004     86.93832  3.95928494    2004_39G9 niche_data  12
#> 283       40G6 1995     42.07800  7.17804636    1995_40G6 niche_data  12
#> 284       40G9 2000     78.00000  3.75535497    2000_40G9 niche_data  12
#> 285       41G6 2005     32.27464  8.00770130    2005_41G6 niche_data  12
#> 286       41G7 1993     38.00000  7.64772485    1993_41G7 niche_data  12
#> 287       42G7 2019     73.23766  5.14020596    2019_42G7 niche_data  12
#> 288       43G6 2015     30.91015  6.59965677    2015_43G6 niche_data  12
#> 289       37G2 2015     14.28574  6.44617012    2015_37G2   all_data  13
#> 290       38G7 2009     21.00000  7.61660133    2009_38G7   all_data  13
#> 291       38G8 2006     40.19787  6.82206017    2006_38G8   all_data  13
#> 292       43G6 2010     30.91015  7.04617512    2010_43G6   all_data  13
#> 293       43G8 2013     27.86322  7.55437913    2013_43G8   all_data  13
#> 294       44G9 2000     98.95766  2.28534828    2000_44G9   all_data  13
#> 295       37G2 2015     14.28574  6.44617012    2015_37G2 niche_data  13
#> 296       38G7 2009     21.00000  7.61660133    2009_38G7 niche_data  13
#> 297       38G8 2006     28.72128  7.09287919    2006_38G8 niche_data  13
#> 298       43G6 2010     30.91015  7.04617512    2010_43G6 niche_data  13
#> 299       43G8 2013     27.21435  7.57535163    2013_43G8 niche_data  13
#> 300       44G9 2000     46.17992  7.00563231    2000_44G9 niche_data  13
#> 301       37G2 2006     14.28574  6.17105576    2006_37G2   all_data  14
#> 302       37G4 2006     13.00000  7.19413739    2006_37G4   all_data  14
#> 303       39G2 2013     19.00000  7.22795842    2013_39G2   all_data  14
#> 304       39G6 2000     68.00000  3.96190424    2000_39G6   all_data  14
#> 305       43G7 2016     83.80202  2.44598831    2016_43G7   all_data  14
#> 306       43G9 2000    180.66532 -1.08761696    2000_43G9   all_data  14
#> 307       37G2 2006     14.28574  6.17105576    2006_37G2 niche_data  14
#> 308       37G4 2006     13.00000  7.19413739    2006_37G4 niche_data  14
#> 309       39G2 2013     19.00000  7.22795842    2013_39G2 niche_data  14
#> 310       39G6 2000     57.87469  5.20242137    2000_39G6 niche_data  14
#> 311       43G7 2016     55.25524  6.08570487    2016_43G7 niche_data  14
#> 312       43G9 2000     64.11560  6.14235763    2000_43G9 niche_data  14
#> 313       37G2 2011     14.28574  6.51160471    2011_37G2   all_data  15
#> 314       37G2 2016     14.28574  6.51976623    2016_37G2   all_data  15
#> 315       37G3 2012     15.00000  7.59774629    2012_37G3   all_data  15
#> 316       37G4 2019     13.00000  7.37146807    2019_37G4   all_data  15
#> 317       39G8 2019     90.00000  4.44147647    2019_39G8   all_data  15
#> 318       39G9 2018     99.00000  2.41810056    2018_39G9   all_data  15
#> 319       43G7 2017     83.80202  0.86235834    2017_43G7   all_data  15
#> 320       43H0 2012    124.04904  1.66832520    2012_43H0   all_data  15
#> 321       44G9 2015     98.95766  1.37900224    2015_44G9   all_data  15
#> 322       37G2 2011     14.28574  6.51160471    2011_37G2 niche_data  15
#> 323       37G2 2016     14.28574  6.51976623    2016_37G2 niche_data  15
#> 324       37G3 2012     15.00000  7.59774629    2012_37G3 niche_data  15
#> 325       37G4 2019     13.00000  7.37146807    2019_37G4 niche_data  15
#> 326       39G8 2019     88.00000  4.66030857    2019_39G8 niche_data  15
#> 327       39G9 2018     84.00000  3.65551893    2018_39G9 niche_data  15
#> 328       43G7 2017     50.84013  5.01262844    2017_43G7 niche_data  15
#> 329       43H0 2012     52.76306  7.49228776    2012_43H0 niche_data  15
#> 330       44G9 2015     44.74013  6.53010692    2015_44G9 niche_data  15
#> 331       38G7 2016     21.00000  7.59702234    2016_38G7   all_data  16
#> 332       39G4 2004     40.00000  4.41765643    2004_39G4   all_data  16
#> 333       39G8 2009     90.00000  3.53176071    2009_39G8   all_data  16
#> 334       38G7 2016     21.00000  7.59702234    2016_38G7 niche_data  16
#> 335       39G4 2004     39.36710  4.56658628    2004_39G4 niche_data  16
#> 336       39G8 2009     88.00000  3.82874807    2009_39G8 niche_data  16
#> 337       37G4 2004     13.00000  7.59149353    2004_37G4   all_data  17
#> 338       43G9 2009    180.66532 -0.60869297    2009_43G9   all_data  17
#> 339       43H1 2001     23.00000  7.59500906    2001_43H1   all_data  17
#> 340       44G9 1995     98.95766  4.27407333    1995_44G9   all_data  17
#> 341       44G9 2016     98.95766  1.57507813    2016_44G9   all_data  17
#> 342       44H1 2005     30.23161  7.68182492    2005_44H1   all_data  17
#> 343       37G4 2004     13.00000  7.59149353    2004_37G4 niche_data  17
#> 344       43G9 2009     65.81358  4.23273200    2009_43G9 niche_data  17
#> 345       43H1 2001     23.00000  7.59500906    2001_43H1 niche_data  17
#> 346       44G9 1995     46.52287  7.56085560    1995_44G9 niche_data  17
#> 347       44G9 2016     44.20763  6.40828282    2016_44G9 niche_data  17
#> 348       44H1 2005     30.23161  7.68182492    2005_44H1 niche_data  17
#> 349       37G6 2009     18.00000  7.53473655    2009_37G6   all_data  18
#> 350       37G6 2011     18.00000  7.47501673    2011_37G6   all_data  18
#> 351       38G7 2011     21.00000  7.44292933    2011_38G7   all_data  18
#> 352       40G8 2019     97.98204  3.59501411    2019_40G8   all_data  18
#> 353       43G6 2006     30.91015  6.63683085    2006_43G6   all_data  18
#> 354       43G7 1998     83.80202  3.06754409    1998_43G7   all_data  18
#> 355       43G8 2003     27.86322  7.77016196    2003_43G8   all_data  18
#> 356       43G9 2011    180.66532 -1.43818252    2011_43G9   all_data  18
#> 357       43G9 2017    180.66532  0.63743157    2017_43G9   all_data  18
#> 358       43G9 2018    180.66532 -0.67414599    2018_43G9   all_data  18
#> 359       43H0 1999    124.04904  1.58474601    1999_43H0   all_data  18
#> 360       44G9 1993     98.95766  5.26415822    1993_44G9   all_data  18
#> 361       44G9 1994     98.95766  3.95200805    1994_44G9   all_data  18
#> 362       44G9 1998     98.95766  1.87044489    1998_44G9   all_data  18
#> 363       37G6 2009     18.00000  7.53473655    2009_37G6 niche_data  18
#> 364       37G6 2011     18.00000  7.47501673    2011_37G6 niche_data  18
#> 365       38G7 2011     21.00000  7.44292933    2011_38G7 niche_data  18
#> 366       40G8 2019     90.00000  3.85075053    2019_40G8 niche_data  18
#> 367       43G6 2006     30.91015  6.63683085    2006_43G6 niche_data  18
#> 368       43G7 1998     56.09113  5.78370064    1998_43G7 niche_data  18
#> 369       43G8 2003     27.22653  7.77632627    2003_43G8 niche_data  18
#> 370       43G9 2011     61.90562  4.90774751    2011_43G9 niche_data  18
#> 371       43G9 2017     60.85209  4.05658256    2017_43G9 niche_data  18
#> 372       43G9 2018     64.11560  4.78981685    2018_43G9 niche_data  18
#> 373       43H0 1999     52.76306  7.54295173    1999_43H0 niche_data  18
#> 374       44G9 1993     46.52287  7.64438296    1993_44G9 niche_data  18
#> 375       44G9 1994     46.52287  7.10462970    1994_44G9 niche_data  18
#> 376       44G9 1998     46.02985  6.43932920    1998_44G9 niche_data  18
#> 377       37G9 2016     43.07566  7.18609526    2016_37G9   all_data  19
#> 378       38G4 1998     25.69453  7.19177706    1998_38G4   all_data  19
#> 379       38G9 2004     91.00641  4.62439524    2004_38G9   all_data  19
#> 380       41G8 2007     96.71104  1.04150892    2007_41G8   all_data  19
#> 381       37G9 2016     43.07566  7.18609526    2016_37G9 niche_data  19
#> 382       38G4 1998     25.69453  7.19177706    1998_38G4 niche_data  19
#> 383       38G9 2004     61.59609  6.75956757    2004_38G9 niche_data  19
#> 384       41G8 2007     36.98351  7.09897959    2007_41G8 niche_data  19
#> 385       38G5 2003     61.20802  4.39397057    2003_38G5   all_data  20
#> 386       39G2 2008     19.00000  6.71568457    2008_39G2   all_data  20
#> 387       41G8 2019     96.71104  2.22479651    2019_41G8   all_data  20
#> 388       42G7 2018     79.04017  3.69379284    2018_42G7   all_data  20
#> 389       44H1 2015     30.23161  7.51325719    2015_44H1   all_data  20
#> 390       38G5 2003     61.20802  4.39397057    2003_38G5 niche_data  20
#> 391       39G2 2008     19.00000  6.71568457    2008_39G2 niche_data  20
#> 392       41G8 2019     39.02779  7.05072587    2019_41G8 niche_data  20
#> 393       42G7 2018     70.80609  4.47713314    2018_42G7 niche_data  20
#> 394       44H1 2015     30.23161  7.51325719    2015_44H1 niche_data  20
#> 395       39G8 2016     90.00000  4.65314756    2016_39G8   all_data  21
#> 396       40G8 2013     97.98204  3.60028984    2013_40G8   all_data  21
#> 397       43G7 2007     83.80202  1.99384919    2007_43G7   all_data  21
#> 398       39G8 2016     88.00000  4.85468840    2016_39G8 niche_data  21
#> 399       40G8 2013     90.00000  4.08673708    2013_40G8 niche_data  21
#> 400       43G7 2007     53.21078  6.49239815    2007_43G7 niche_data  21
#> 401       38G7 2015     21.00000  7.51673282    2015_38G7   all_data  22
#> 402       40G6 2002     50.04572  6.74227730    2002_40G6   all_data  22
#> 403       41G7 2005     38.00000  7.25772993    2005_41G7   all_data  22
#> 404       42G6 2007     29.09908  7.57437836    2007_42G6   all_data  22
#> 405       43H0 2002    124.04904  2.03749340    2002_43H0   all_data  22
#> 406       38G7 2015     21.00000  7.51673282    2015_38G7 niche_data  22
#> 407       40G6 2002     41.81499  7.66965984    2002_40G6 niche_data  22
#> 408       41G7 2005     38.00000  7.25772993    2005_41G7 niche_data  22
#> 409       42G6 2007     29.09908  7.57437836    2007_42G6 niche_data  22
#> 410       43H0 2002     52.76306  7.71198503    2002_43H0 niche_data  22
#> 411       37G2 2017     14.28574  7.02884756    2017_37G2   all_data  23
#> 412       37G3 2016     15.00000  7.75143729    2016_37G3   all_data  23
#> 413       37G4 2014     13.00000  7.47052818    2014_37G4   all_data  23
#> 414       37G9 2003     43.07566  6.59708352    2003_37G9   all_data  23
#> 415       38G7 2019     21.00000  7.34577553    2019_38G7   all_data  23
#> 416       38G8 2010     40.19787  6.95818065    2010_38G8   all_data  23
#> 417       39G7 2016     56.11417  6.29210768    2016_39G7   all_data  23
#> 418       41G8 2011     96.71104  1.71872021    2011_41G8   all_data  23
#> 419       42G6 2011     29.09908  7.04046690    2011_42G6   all_data  23
#> 420       44H1 2000     30.23161  7.43332338    2000_44H1   all_data  23
#> 421       37G2 2017     14.28574  7.02884756    2017_37G2 niche_data  23
#> 422       37G3 2016     15.00000  7.75143729    2016_37G3 niche_data  23
#> 423       37G4 2014     13.00000  7.47052818    2014_37G4 niche_data  23
#> 424       37G9 2003     43.07566  6.59708352    2003_37G9 niche_data  23
#> 425       38G7 2019     21.00000  7.34577553    2019_38G7 niche_data  23
#> 426       38G8 2010     28.72128  7.32960116    2010_38G8 niche_data  23
#> 427       39G7 2016     52.79956  6.37231526    2016_39G7 niche_data  23
#> 428       41G8 2011     38.87665  7.16817038    2011_41G8 niche_data  23
#> 429       42G6 2011     28.06362  7.23311136    2011_42G6 niche_data  23
#> 430       44H1 2000     30.23161  7.43332338    2000_44H1 niche_data  23
#> 431       37G2 2010     14.28574  6.92355355    2010_37G2   all_data  24
#> 432       38G2 2015     17.00000  6.87485935    2015_38G2   all_data  24
#> 433       38G7 2002     21.00000  7.71498493    2002_38G7   all_data  24
#> 434       41G7 2018     38.00000  7.24367285    2018_41G7   all_data  24
#> 435       41H0 2000     28.04549  7.44750256    2000_41H0   all_data  24
#> 436       44G9 2011     98.95766  1.63250978    2011_44G9   all_data  24
#> 437       37G2 2010     14.28574  6.92355355    2010_37G2 niche_data  24
#> 438       38G2 2015     17.00000  6.87485935    2015_38G2 niche_data  24
#> 439       38G7 2002     21.00000  7.71498493    2002_38G7 niche_data  24
#> 440       41G7 2018     38.00000  7.24367285    2018_41G7 niche_data  24
#> 441       41H0 2000     28.00000  7.46003752    2000_41H0 niche_data  24
#> 442       44G9 2011     45.86368  6.64383169    2011_44G9 niche_data  24
#> 443       37G2 2019     14.28574  6.55959956    2019_37G2   all_data  25
#> 444       39G5 1999     85.00000  0.62833199    1999_39G5   all_data  25
#> 445       40G7 2004     38.00000  7.67239285    2004_40G7   all_data  25
#> 446       41G9 2011    123.90523  0.72067670    2011_41G9   all_data  25
#> 447       42G6 2001     29.09908  7.55267871    2001_42G6   all_data  25
#> 448       37G2 2019     14.28574  6.55959956    2019_37G2 niche_data  25
#> 449       39G5 1999     76.00000  2.89805908    1999_39G5 niche_data  25
#> 450       40G7 2004     38.00000  7.67239285    2004_40G7 niche_data  25
#> 451       41G9 2011     59.00000  7.52109967    2011_41G9 niche_data  25
#> 452       42G6 2001     29.09908  7.55267871    2001_42G6 niche_data  25
#> 453       37G3 2018     15.00000  7.70093912    2018_37G3   all_data  26
#> 454       37G9 2008     43.07566  6.89118340    2008_37G9   all_data  26
#> 455       38G7 2013     21.00000  7.61324480    2013_38G7   all_data  26
#> 456       39G2 2019     19.00000  7.32731260    2019_39G2   all_data  26
#> 457       39G4 2012     40.00000  4.33762036    2012_39G4   all_data  26
#> 458       43G8 2018     27.86322  7.60018025    2018_43G8   all_data  26
#> 459       43H1 2003     23.00000  7.32968260    2003_43H1   all_data  26
#> 460       44G9 2017     98.95766  1.58633100    2017_44G9   all_data  26
#> 461       37G3 2018     15.00000  7.70093912    2018_37G3 niche_data  26
#> 462       37G9 2008     43.07566  6.89118340    2008_37G9 niche_data  26
#> 463       38G7 2013     21.00000  7.61324480    2013_38G7 niche_data  26
#> 464       39G2 2019     19.00000  7.32731260    2019_39G2 niche_data  26
#> 465       39G4 2012     40.00000  4.45442415    2012_39G4 niche_data  26
#> 466       43G8 2018     27.22653  7.61920332    2018_43G8 niche_data  26
#> 467       43H1 2003     23.00000  7.32968260    2003_43H1 niche_data  26
#> 468       44G9 2017     45.24342  5.39958522    2017_44G9 niche_data  26
#> 469       43G7 2005     83.80202  3.53961841    2005_43G7   all_data  27
#> 470       43H0 2008    124.04904  2.55146042    2008_43H0   all_data  27
#> 471       43G7 2005     56.05859  7.00992761    2005_43G7 niche_data  27
#> 472       43H0 2008     52.76306  7.59118411    2008_43H0 niche_data  27
#> 473       37G3 2014     15.00000  7.36849369    2014_37G3   all_data  28
#> 474       37G4 2011     13.00000  7.50200086    2011_37G4   all_data  28
#> 475       37G4 2012     13.00000  7.67865801    2012_37G4   all_data  28
#> 476       38G3 2002     33.62888  6.59671682    2002_38G3   all_data  28
#> 477       41G6 2007     32.27464  7.37435497    2007_41G6   all_data  28
#> 478       42G6 2008     29.09908  7.31306009    2008_42G6   all_data  28
#> 479       43H0 2013    124.04904  2.89247954    2013_43H0   all_data  28
#> 480       37G3 2014     15.00000  7.36849369    2014_37G3 niche_data  28
#> 481       37G4 2011     13.00000  7.50200086    2011_37G4 niche_data  28
#> 482       37G4 2012     13.00000  7.67865801    2012_37G4 niche_data  28
#> 483       38G3 2002     33.62888  6.59671682    2002_38G3 niche_data  28
#> 484       41G6 2007     32.27464  7.37435497    2007_41G6 niche_data  28
#> 485       42G6 2008     29.09908  7.31306009    2008_42G6 niche_data  28
#> 486       43H0 2013     52.76306  7.66680368    2013_43H0 niche_data  28
#> 487       39G4 2003     40.00000  4.02255464    2003_39G4   all_data  29
#> 488       39G8 2003     90.00000  4.76394612    2003_39G8   all_data  29
#> 489       40G7 2019     38.00000  7.38617128    2019_40G7   all_data  29
#> 490       40G8 2009     97.98204  2.41834606    2009_40G8   all_data  29
#> 491       41G9 2012    123.90523  1.49488254    2012_41G9   all_data  29
#> 492       43G8 2010     27.86322  7.91113002    2010_43G8   all_data  29
#> 493       39G4 2003     39.68355  4.21874870    2003_39G4 niche_data  29
#> 494       39G8 2003     88.00000  4.94968540    2003_39G8 niche_data  29
#> 495       40G7 2019     38.00000  7.38617128    2019_40G7 niche_data  29
#> 496       40G8 2009     89.79780  2.92027646    2009_40G8 niche_data  29
#> 497       41G9 2012     59.00000  7.33802408    2012_41G9 niche_data  29
#> 498       43G8 2010     27.21435  7.97784998    2010_43G8 niche_data  29
#> 499       37G5 2016     20.00000  7.69209965    2016_37G5   all_data  30
#> 500       39G5 2003     85.00000  1.72726266    2003_39G5   all_data  30
#> 501       39G8 2007     90.00000  3.07713978    2007_39G8   all_data  30
#> 502       40G6 1997     50.04572  6.34036354    1997_40G6   all_data  30
#> 503       43G7 2014     83.80202  2.28003095    2014_43G7   all_data  30
#> 504       44H1 2007     30.23161  7.51872898    2007_44H1   all_data  30
#> 505       37G5 2016     20.00000  7.69209965    2016_37G5 niche_data  30
#> 506       39G5 2003     79.00000  2.42228395    2003_39G5 niche_data  30
#> 507       39G8 2007     88.00000  3.53354249    2007_39G8 niche_data  30
#> 508       40G6 1997     42.00000  7.61180228    1997_40G6 niche_data  30
#> 509       43G7 2014     55.14670  5.25642892    2014_43G7 niche_data  30
#> 510       44H1 2007     30.23161  7.51872898    2007_44H1 niche_data  30
#> 511       37G5 2018     20.00000  7.42856248    2018_37G5   all_data  31
#> 512       38G6 2003     26.52553  7.24461377    2003_38G6   all_data  31
#> 513       39G8 2018     90.00000  4.08800536    2018_39G8   all_data  31
#> 514       37G5 2018     20.00000  7.42856248    2018_37G5 niche_data  31
#> 515       38G6 2003     26.52553  7.24461377    2003_38G6 niche_data  31
#> 516       39G8 2018     88.00000  4.37510083    2018_39G8 niche_data  31
#> 517       39G3 2010     34.94275  6.18220260    2010_39G3   all_data  32
#> 518       39G5 2013     85.00000  0.84231551    2013_39G5   all_data  32
#> 519       39G8 2013     90.00000  4.81218219    2013_39G8   all_data  32
#> 520       41H0 2016     28.04549  7.78980493    2016_41H0   all_data  32
#> 521       43G7 2010     83.80202  2.91216648    2010_43G7   all_data  32
#> 522       43G9 1994    180.66532  1.81492877    1994_43G9   all_data  32
#> 523       44G9 2005     98.95766  2.57242856    2005_44G9   all_data  32
#> 524       39G3 2010     34.94275  6.18220260    2010_39G3 niche_data  32
#> 525       39G5 2013     75.62254  3.04269892    2013_39G5 niche_data  32
#> 526       39G8 2013     88.00000  4.98153006    2013_39G8 niche_data  32
#> 527       41H0 2016     28.00000  7.79412197    2016_41H0 niche_data  32
#> 528       43G7 2010     55.40681  5.78297012    2010_43G7 niche_data  32
#> 529       43G9 1994     66.36603  5.84348783    1994_43G9 niche_data  32
#> 530       44G9 2005     46.52287  6.71606378    2005_44G9 niche_data  32
#> 531       40G6 2003     50.04572  6.48888767    2003_40G6   all_data  33
#> 532       43G8 1994     27.86322  7.97612965    1994_43G8   all_data  33
#> 533       43G9 2007    180.66532  0.34327510    2007_43G9   all_data  33
#> 534       44G9 2009     98.95766  1.25805954    2009_44G9   all_data  33
#> 535       44G9 2014     98.95766  1.36514297    2014_44G9   all_data  33
#> 536       44H1 2017     30.23161  7.52830413    2017_44H1   all_data  33
#> 537       40G6 2003     50.04572  6.48888767    2003_40G6 niche_data  33
#> 538       43G8 1994     27.22653  7.99703374    1994_43G8 niche_data  33
#> 539       43G9 2007     65.81358  4.03103899    2007_43G9 niche_data  33
#> 540       44G9 2009     44.74013  5.66468196    2009_44G9 niche_data  33
#> 541       44G9 2014     45.86368  6.69865355    2014_44G9 niche_data  33
#> 542       44H1 2017     30.23161  7.52830413    2017_44H1 niche_data  33
#> 543       37G5 2007     20.00000  7.37621541    2007_37G5   all_data  34
#> 544       38G8 2018     40.19787  6.31401725    2018_38G8   all_data  34
#> 545       38G9 2013     91.00641  4.76076835    2013_38G9   all_data  34
#> 546       39G5 2002     85.00000  0.13054928    2002_39G5   all_data  34
#> 547       41G7 2013     38.00000  7.47324005    2013_41G7   all_data  34
#> 548       41G7 2019     38.00000  7.02059156    2019_41G7   all_data  34
#> 549       41G9 2016    123.90523  4.12806278    2016_41G9   all_data  34
#> 550       41H0 1999     28.04549  7.75664200    1999_41H0   all_data  34
#> 551       42G6 2010     29.09908  7.71073086    2010_42G6   all_data  34
#> 552       43G8 2009     27.86322  7.61786033    2009_43G8   all_data  34
#> 553       44H1 2016     30.23161  7.80635924    2016_44H1   all_data  34
#> 554       37G5 2007     20.00000  7.37621541    2007_37G5 niche_data  34
#> 555       38G8 2018     28.72128  6.85725734    2018_38G8 niche_data  34
#> 556       38G9 2013     61.59609  6.72872311    2013_38G9 niche_data  34
#> 557       39G5 2002     70.00000  4.11245169    2002_39G5 niche_data  34
#> 558       41G7 2013     38.00000  7.47324005    2013_41G7 niche_data  34
#> 559       41G7 2019     38.00000  7.02059156    2019_41G7 niche_data  34
#> 560       41G9 2016     59.00000  6.50595188    2016_41G9 niche_data  34
#> 561       41H0 1999     28.00000  7.76052457    1999_41H0 niche_data  34
#> 562       42G6 2010     29.09908  7.71073086    2010_42G6 niche_data  34
#> 563       43G8 2009     27.21435  7.65588453    2009_43G8 niche_data  34
#> 564       44H1 2016     30.23161  7.80635924    2016_44H1 niche_data  34
#> 565       38G2 2016     17.00000  6.87888888    2016_38G2   all_data  35
#> 566       38G9 2012     91.00641  3.97631938    2012_38G9   all_data  35
#> 567       39G2 2016     19.00000  7.19597008    2016_39G2   all_data  35
#> 568       39G7 2019     56.11417  6.15882848    2019_39G7   all_data  35
#> 569       38G2 2016     17.00000  6.87888888    2016_38G2 niche_data  35
#> 570       38G9 2012     61.59609  5.82741988    2012_38G9 niche_data  35
#> 571       39G2 2016     19.00000  7.19597008    2016_39G2 niche_data  35
#> 572       39G7 2019     52.79956  6.27296001    2019_39G7 niche_data  35
#> 573       37G3 2011     15.00000  7.30103238    2011_37G3   all_data  36
#> 574       37G5 2019     20.00000  7.24426451    2019_37G5   all_data  36
#> 575       37G3 2011     15.00000  7.30103238    2011_37G3 niche_data  36
#> 576       37G5 2019     20.00000  7.24426451    2019_37G5 niche_data  36
#> 577       37G2 2012     14.28574  6.68620991    2012_37G2   all_data  37
#> 578       38G9 2014     91.00641  5.39271436    2014_38G9   all_data  37
#> 579       38G9 2019     91.00641  5.30043535    2019_38G9   all_data  37
#> 580       39G7 2017     56.11417  6.28875133    2017_39G7   all_data  37
#> 581       42H0 2013     57.59084  7.45001335    2013_42H0   all_data  37
#> 582       37G2 2012     14.28574  6.68620991    2012_37G2 niche_data  37
#> 583       38G9 2014     61.59609  6.25590794    2014_38G9 niche_data  37
#> 584       38G9 2019     61.59609  6.42686382    2019_38G9 niche_data  37
#> 585       39G7 2017     52.79956  6.38260568    2017_39G7 niche_data  37
#> 586       42H0 2013     43.82713  7.70013848    2013_42H0 niche_data  37
#> 587       38G2 2010     17.00000  7.29131447    2010_38G2   all_data  38
#> 588       38G4 1996     25.69453  6.91677826    1996_38G4   all_data  38
#> 589       40G7 1993     38.00000  7.80536675    1993_40G7   all_data  38
#> 590       38G2 2010     17.00000  7.29131447    2010_38G2 niche_data  38
#> 591       38G4 1996     25.69453  6.91677826    1996_38G4 niche_data  38
#> 592       40G7 1993     38.00000  7.80536675    1993_40G7 niche_data  38
#> 593       37G3 2019     15.00000  7.42280702    2019_37G3   all_data  39
#> 594       39G6 2010     68.00000  4.43348591    2010_39G6   all_data  39
#> 595       37G3 2019     15.00000  7.42280702    2019_37G3 niche_data  39
#> 596       39G6 2010     64.61201  4.70200837    2010_39G6 niche_data  39
#> 597       37G3 2009     15.00000  7.27104243    2009_37G3   all_data  40
#> 598       38G3 2005     33.62888  6.03707738    2005_38G3   all_data  40
#> 599       39G5 2012     85.00000 -0.11445277    2012_39G5   all_data  40
#> 600       40G4 1999     39.57072  7.21968820    1999_40G4   all_data  40
#> 601       40G6 2000     50.04572  6.80037369    2000_40G6   all_data  40
#> 602       42G6 2002     29.09908  7.76038307    2002_42G6   all_data  40
#> 603       37G3 2009     15.00000  7.27104243    2009_37G3 niche_data  40
#> 604       38G3 2005     33.00000  6.09789592    2005_38G3 niche_data  40
#> 605       39G5 2012     65.31756  3.41750354    2012_39G5 niche_data  40
#> 606       40G4 1999     36.62094  7.60203131    1999_40G4 niche_data  40
#> 607       40G6 2000     42.07800  7.26315495    2000_40G6 niche_data  40
#> 608       42G6 2002     29.09908  7.76038307    2002_42G6 niche_data  40
#> 609       37G6 2019     18.00000  7.28054888    2019_37G6   all_data  41
#> 610       39G3 2003     34.94275  5.44739639    2003_39G3   all_data  41
#> 611       37G6 2019     18.00000  7.28054888    2019_37G6 niche_data  41
#> 612       39G3 2003     34.94275  5.44739639    2003_39G3 niche_data  41
#> 613       38G7 2004     21.00000  7.66614957    2004_38G7   all_data  42
#> 614       39G2 2000     19.00000  7.27781077    2000_39G2   all_data  42
#> 615       38G7 2004     21.00000  7.66614957    2004_38G7 niche_data  42
#> 616       39G2 2000     19.00000  7.27781077    2000_39G2 niche_data  42
#> 617       37G3 2013     15.00000  7.67531611    2013_37G3   all_data  43
#> 618       37G4 2015     13.00000  7.57578846    2015_37G4   all_data  43
#> 619       37G6 2008     18.00000  7.56334757    2008_37G6   all_data  43
#> 620       37G9 2011     43.07566  6.98702971    2011_37G9   all_data  43
#> 621       39G2 2018     19.00000  7.28550305    2018_39G2   all_data  43
#> 622       39G8 2001     90.00000  3.60947449    2001_39G8   all_data  43
#> 623       41G8 2012     96.71104  1.66211530    2012_41G8   all_data  43
#> 624       43G8 2019     27.86322  7.60874543    2019_43G8   all_data  43
#> 625       37G3 2013     15.00000  7.67531611    2013_37G3 niche_data  43
#> 626       37G4 2015     13.00000  7.57578846    2015_37G4 niche_data  43
#> 627       37G6 2008     18.00000  7.56334757    2008_37G6 niche_data  43
#> 628       37G9 2011     43.07566  6.98702971    2011_37G9 niche_data  43
#> 629       39G2 2018     19.00000  7.28550305    2018_39G2 niche_data  43
#> 630       39G8 2001     88.00000  3.88911372    2001_39G8 niche_data  43
#> 631       41G8 2012     38.87665  6.70193433    2012_41G8 niche_data  43
#> 632       43G8 2019     27.22653  7.63421668    2019_43G8 niche_data  43
#> 633       37G5 2013     20.00000  7.84677466    2013_37G5   all_data  44
#> 634       38G3 2006     33.62888  6.06132920    2006_38G3   all_data  44
#> 635       38G3 2007     33.62888  6.45386001    2007_38G3   all_data  44
#> 636       38G3 2008     33.62888  6.12695620    2008_38G3   all_data  44
#> 637       41G6 2016     32.27464  7.25754807    2016_41G6   all_data  44
#> 638       41G7 1996     38.00000  7.67789125    1996_41G7   all_data  44
#> 639       43G8 1993     27.86322  7.89559423    1993_43G8   all_data  44
#> 640       43H1 2018     23.00000  7.53235972    2018_43H1   all_data  44
#> 641       37G5 2013     20.00000  7.84677466    2013_37G5 niche_data  44
#> 642       38G3 2006     31.00000  6.12357967    2006_38G3 niche_data  44
#> 643       38G3 2007     33.62888  6.45386001    2007_38G3 niche_data  44
#> 644       38G3 2008     33.62888  6.12695620    2008_38G3 niche_data  44
#> 645       41G6 2016     32.27464  7.25754807    2016_41G6 niche_data  44
#> 646       41G7 1996     38.00000  7.67789125    1996_41G7 niche_data  44
#> 647       43G8 1993     27.22653  7.90188212    1993_43G8 niche_data  44
#> 648       43H1 2018     23.00000  7.53235972    2018_43H1 niche_data  44
#> 649       37G2 2008     14.28574  6.33305973    2008_37G2   all_data  45
#> 650       37G5 2017     20.00000  7.63393050    2017_37G5   all_data  45
#> 651       40G7 2011     38.00000  7.65464447    2011_40G7   all_data  45
#> 652       42G6 2014     29.09908  7.13682141    2014_42G6   all_data  45
#> 653       43G7 2008     83.80202  2.40867153    2008_43G7   all_data  45
#> 654       43H0 2011    124.04904  1.99002023    2011_43H0   all_data  45
#> 655       37G2 2008     14.28574  6.33305973    2008_37G2 niche_data  45
#> 656       37G5 2017     20.00000  7.63393050    2017_37G5 niche_data  45
#> 657       40G7 2011     38.00000  7.65464447    2011_40G7 niche_data  45
#> 658       42G6 2014     27.40247  7.14134771    2014_42G6 niche_data  45
#> 659       43G7 2008     55.40681  5.53785205    2008_43G7 niche_data  45
#> 660       43H0 2011     52.76306  7.25095358    2011_43H0 niche_data  45
#> 661       37G5 2004     20.00000  7.63559580    2004_37G5   all_data  46
#> 662       38G6 2017     26.52553  7.49274324    2017_38G6   all_data  46
#> 663       40G6 1993     50.04572  7.05463041    1993_40G6   all_data  46
#> 664       42G6 2016     29.09908  7.68722011    2016_42G6   all_data  46
#> 665       44H1 1997     30.23161  7.69056932    1997_44H1   all_data  46
#> 666       37G5 2004     20.00000  7.63559580    2004_37G5 niche_data  46
#> 667       38G6 2017     26.52553  7.49274324    2017_38G6 niche_data  46
#> 668       40G6 1993     50.04572  7.05463041    1993_40G6 niche_data  46
#> 669       42G6 2016     29.09908  7.68722011    2016_42G6 niche_data  46
#> 670       44H1 1997     30.23161  7.69056932    1997_44H1 niche_data  46
#> 671       39G2 2015     19.00000  7.03724911    2015_39G2   all_data  47
#> 672       41G8 1996     96.71104  3.05839566    1996_41G8   all_data  47
#> 673       43H1 1999     23.00000  7.63820482    1999_43H1   all_data  47
#> 674       39G2 2015     19.00000  7.03724911    2015_39G2 niche_data  47
#> 675       41G8 1996     39.05557  7.69750979    1996_41G8 niche_data  47
#> 676       43H1 1999     23.00000  7.63820482    1999_43H1 niche_data  47
#> 677       38G4 1997     25.69453  6.78667273    1997_38G4   all_data  48
#> 678       39G7 2011     56.11417  6.35559373    2011_39G7   all_data  48
#> 679       40G7 2012     38.00000  7.27593234    2012_40G7   all_data  48
#> 680       43G7 1997     83.80202  2.52443988    1997_43G7   all_data  48
#> 681       43H1 2005     23.00000  7.53644505    2005_43H1   all_data  48
#> 682       38G4 1997     25.42107  6.80087925    1997_38G4 niche_data  48
#> 683       39G7 2011     52.79956  6.53990964    2011_39G7 niche_data  48
#> 684       40G7 2012     38.00000  7.27593234    2012_40G7 niche_data  48
#> 685       43G7 1997     56.05859  6.65167254    1997_43G7 niche_data  48
#> 686       43H1 2005     23.00000  7.53644505    2005_43H1 niche_data  48
#> 687       38G2 2008     17.00000  6.61531852    2008_38G2   all_data  49
#> 688       40G4 1993     39.57072  7.71440667    1993_40G4   all_data  49
#> 689       40G6 2010     50.04572  5.98055403    2010_40G6   all_data  49
#> 690       41G9 2015    123.90523  3.07408980    2015_41G9   all_data  49
#> 691       41H0 2008     28.04549  7.64133225    2008_41H0   all_data  49
#> 692       41H0 2010     28.04549  7.69010563    2010_41H0   all_data  49
#> 693       42G7 2002     79.04017  4.64961992    2002_42G7   all_data  49
#> 694       43H0 2018    124.04904  1.96776634    2018_43H0   all_data  49
#> 695       43H0 2019    124.04904  2.66846332    2019_43H0   all_data  49
#> 696       44G9 2004     98.95766  1.63551309    2004_44G9   all_data  49
#> 697       44G9 2010     98.95766  1.27792146    2010_44G9   all_data  49
#> 698       38G2 2008     17.00000  6.61531852    2008_38G2 niche_data  49
#> 699       40G4 1993     39.57072  7.71440667    1993_40G4 niche_data  49
#> 700       40G6 2010     50.00000  6.07982886    2010_40G6 niche_data  49
#> 701       41G9 2015     59.00000  6.61341739    2015_41G9 niche_data  49
#> 702       41H0 2008     28.00000  7.64406003    2008_41H0 niche_data  49
#> 703       41H0 2010     28.00000  7.69470219    2010_41H0 niche_data  49
#> 704       42G7 2002     70.80609  5.95574731    2002_42G7 niche_data  49
#> 705       43H0 2018     52.76306  6.85510945    2018_43H0 niche_data  49
#> 706       43H0 2019     52.76306  7.35617200    2019_43H0 niche_data  49
#> 707       44G9 2004     45.98064  5.96571702    2004_44G9 niche_data  49
#> 708       44G9 2010     43.98159  6.67564652    2010_44G9 niche_data  49
#> 709       37G2 2009     14.28574  6.33964201    2009_37G2   all_data  50
#> 710       38G6 2007     26.52553  7.28364843    2007_38G6   all_data  50
#> 711       38G6 2016     26.52553  7.53210033    2016_38G6   all_data  50
#> 712       39G2 2002     19.00000  7.42134696    2002_39G2   all_data  50
#> 713       40G8 1995     97.98204  3.19156659    1995_40G8   all_data  50
#> 714       37G2 2009     14.28574  6.33964201    2009_37G2 niche_data  50
#> 715       38G6 2007     26.52553  7.28364843    2007_38G6 niche_data  50
#> 716       38G6 2016     26.52553  7.53210033    2016_38G6 niche_data  50
#> 717       39G2 2002     19.00000  7.42134696    2002_39G2 niche_data  50
#> 718       40G8 1995     90.00000  4.16898024    1995_40G8 niche_data  50
#> 719       37G4 2016     13.00000  7.75155089    2016_37G4   all_data  51
#> 720       37G9 2007     43.07566  6.64369882    2007_37G9   all_data  51
#> 721       38G2 1999     17.00000  6.65221237    1999_38G2   all_data  51
#> 722       38G5 2012     61.20802  4.70633050    2012_38G5   all_data  51
#> 723       40G7 2010     38.00000  7.35010622    2010_40G7   all_data  51
#> 724       37G4 2016     13.00000  7.75155089    2016_37G4 niche_data  51
#> 725       37G9 2007     43.07566  6.64369882    2007_37G9 niche_data  51
#> 726       38G2 1999     17.00000  6.65221237    1999_38G2 niche_data  51
#> 727       38G5 2012     58.00000  5.00115176    2012_38G5 niche_data  51
#> 728       40G7 2010     38.00000  7.35010622    2010_40G7 niche_data  51
#> 729       37G9 2005     43.07566  7.00414210    2005_37G9   all_data  52
#> 730       37G9 2019     43.07566  6.79033019    2019_37G9   all_data  52
#> 731       38G4 2002     25.69453  6.89068405    2002_38G4   all_data  52
#> 732       39G6 2009     68.00000  3.08551402    2009_39G6   all_data  52
#> 733       41G7 2014     38.00000  7.35256902    2014_41G7   all_data  52
#> 734       44G9 2012     98.95766  1.97364078    2012_44G9   all_data  52
#> 735       37G9 2005     43.07566  7.00414210    2005_37G9 niche_data  52
#> 736       37G9 2019     43.07566  6.79033019    2019_37G9 niche_data  52
#> 737       38G4 2002     25.69453  6.89068405    2002_38G4 niche_data  52
#> 738       39G6 2009     58.36765  3.97949024    2009_39G6 niche_data  52
#> 739       41G7 2014     38.00000  7.35256902    2014_41G7 niche_data  52
#> 740       44G9 2012     46.02985  6.77895067    2012_44G9 niche_data  52
#> 741       38G2 2014     17.00000  6.62595913    2014_38G2   all_data  53
#> 742       38G5 2007     61.20802  3.63475481    2007_38G5   all_data  53
#> 743       39G8 2012     90.00000  4.33266205    2012_39G8   all_data  53
#> 744       43H1 2010     23.00000  7.76899695    2010_43H1   all_data  53
#> 745       38G2 2014     17.00000  6.62595913    2014_38G2 niche_data  53
#> 746       38G5 2007     58.14054  3.84897167    2007_38G5 niche_data  53
#> 747       39G8 2012     88.00000  4.62738483    2012_39G8 niche_data  53
#> 748       43H1 2010     23.00000  7.76899695    2010_43H1 niche_data  53
#> 749       43G7 2018     83.80202  2.52154931    2018_43G7   all_data  54
#> 750       43G7 2018     56.08167  6.24095591    2018_43G7 niche_data  54
#> 751       39G5 2005     85.00000  0.08046261    2005_39G5   all_data  55
#> 752       41G9 2010    123.90523  2.55796972    2010_41G9   all_data  55
#> 753       39G5 2005     73.12706  3.13436418    2005_39G5 niche_data  55
#> 754       41G9 2010     59.00000  6.49399040    2010_41G9 niche_data  55
#> 755       37G3 2010     15.00000  7.80242944    2010_37G3   all_data  56
#> 756       39G2 2007     19.00000  7.31536951    2007_39G2   all_data  56
#> 757       40G6 1999     50.04572  6.23477568    1999_40G6   all_data  56
#> 758       44G9 2018     98.95766  1.68529398    2018_44G9   all_data  56
#> 759       37G3 2010     15.00000  7.80242944    2010_37G3 niche_data  56
#> 760       39G2 2007     19.00000  7.31536951    2007_39G2 niche_data  56
#> 761       40G6 1999     45.38945  6.97704009    1999_40G6 niche_data  56
#> 762       44G9 2018     46.02985  6.33603857    2018_44G9 niche_data  56
#> 763       39G6 2005     68.00000  3.18059539    2005_39G6   all_data  57
#> 764       39G6 2005     58.18382  4.46433408    2005_39G6 niche_data  57
#> 765       38G6 2009     26.52553  7.42897414    2009_38G6   all_data  58
#> 766       39G3 2014     34.94275  5.65621522    2014_39G3   all_data  58
#> 767       40G6 2008     50.04572  6.02251583    2008_40G6   all_data  58
#> 768       43G9 2010    180.66532 -1.03082651    2010_43G9   all_data  58
#> 769       38G6 2009     26.52553  7.42897414    2009_38G6 niche_data  58
#> 770       39G3 2014     34.94275  5.65621522    2014_39G3 niche_data  58
#> 771       40G6 2008     42.80304  6.92606231    2008_40G6 niche_data  58
#> 772       43G9 2010     62.41762  4.72454055    2010_43G9 niche_data  58
#> 773       39G8 2006     90.00000  3.90001795    2006_39G8   all_data  59
#> 774       39G8 2006     88.00000  4.20148195    2006_39G8 niche_data  59
#> 775       37G9 2009     43.07566  6.56675381    2009_37G9   all_data  60
#> 776       39G7 2018     56.11417  5.84309600    2018_39G7   all_data  60
#> 777       44G9 2001     98.95766  1.65110720    2001_44G9   all_data  60
#> 778       37G9 2009     43.07566  6.56675381    2009_37G9 niche_data  60
#> 779       39G7 2018     52.79956  6.16116262    2018_39G7 niche_data  60
#> 780       44G9 2001     46.02985  7.44970465    2001_44G9 niche_data  60
#> 781       38G4 2005     25.69453  6.82419797    2005_38G4   all_data  61
#> 782       38G8 2013     40.19787  6.84336419    2013_38G8   all_data  61
#> 783       43G8 2015     27.86322  7.42647186    2015_43G8   all_data  61
#> 784       38G4 2005     25.69453  6.82419797    2005_38G4 niche_data  61
#> 785       38G8 2013     28.72128  7.10332325    2013_38G8 niche_data  61
#> 786       43G8 2015     27.20216  7.48788713    2015_43G8 niche_data  61
#> 787       37G2 2003     14.28574  6.05489560    2003_37G2   all_data  62
#> 788       39G3 2001     34.94275  5.87019448    2001_39G3   all_data  62
#> 789       40G5 1994     62.00000  4.04998070    1994_40G5   all_data  62
#> 790       40G9 2003     83.07490  3.62139231    2003_40G9   all_data  62
#> 791       37G2 2003     14.28574  6.05489560    2003_37G2 niche_data  62
#> 792       39G3 2001     34.94275  5.87019448    2001_39G3 niche_data  62
#> 793       40G5 1994     54.04199  5.44878211    1994_40G5 niche_data  62
#> 794       40G9 2003     82.00000  3.62232346    2003_40G9 niche_data  62
#> 795       38G2 2011     17.00000  7.03788224    2011_38G2   all_data  63
#> 796       39G7 2000     56.11417  6.11579497    2000_39G7   all_data  63
#> 797       40G4 1996     39.57072  7.17800898    1996_40G4   all_data  63
#> 798       40G7 1998     38.00000  7.30520588    1998_40G7   all_data  63
#> 799       43H0 1997    124.04904  4.30876146    1997_43H0   all_data  63
#> 800       43H0 2001    124.04904  2.58104605    2001_43H0   all_data  63
#> 801       38G2 2011     17.00000  7.03788224    2011_38G2 niche_data  63
#> 802       39G7 2000     52.79956  6.34742538    2000_39G7 niche_data  63
#> 803       40G4 1996     38.42288  7.22931341    1996_40G4 niche_data  63
#> 804       40G7 1998     38.00000  7.30520588    1998_40G7 niche_data  63
#> 805       43H0 1997     52.76306  7.54814547    1997_43H0 niche_data  63
#> 806       43H0 2001     52.76306  7.44067509    2001_43H0 niche_data  63
#> 807       39G7 2013     56.11417  7.02399348    2013_39G7   all_data  64
#> 808       41G7 1997     38.00000  7.60472610    1997_41G7   all_data  64
#> 809       41G7 2000     38.00000  7.04236606    2000_41G7   all_data  64
#> 810       42G6 2009     29.09908  7.70694377    2009_42G6   all_data  64
#> 811       43G7 1994     83.80202  3.44017471    1994_43G7   all_data  64
#> 812       39G7 2013     52.79956  7.19950744    2013_39G7 niche_data  64
#> 813       41G7 1997     38.00000  7.60472610    1997_41G7 niche_data  64
#> 814       41G7 2000     38.00000  7.04236606    2000_41G7 niche_data  64
#> 815       42G6 2009     29.09908  7.70694377    2009_42G6 niche_data  64
#> 816       43G7 1994     60.79264  5.86167718    1994_43G7 niche_data  64
#> 817       43G6 2008     30.91015  5.44511431    2008_43G6   all_data  65
#> 818       43G6 2008     30.91015  5.44511431    2008_43G6 niche_data  65
#> 819       39G3 2017     34.94275  4.96212097    2017_39G3   all_data  66
#> 820       39G8 2017     90.00000  4.25414202    2017_39G8   all_data  66
#> 821       42H0 1999     57.59084  6.99641328    1999_42H0   all_data  66
#> 822       43G7 2000     83.80202  4.11967488    2000_43G7   all_data  66
#> 823       43G8 1996     27.86322  7.76414524    1996_43G8   all_data  66
#> 824       43H1 2011     23.00000  7.49277451    2011_43H1   all_data  66
#> 825       39G3 2017     34.94275  4.96212097    2017_39G3 niche_data  66
#> 826       39G8 2017     88.00000  4.57112959    2017_39G8 niche_data  66
#> 827       42H0 1999     43.82713  7.61822425    1999_42H0 niche_data  66
#> 828       43G7 2000     56.76395  7.40986627    2000_43G7 niche_data  66
#> 829       43G8 1996     27.22653  7.77148566    1996_43G8 niche_data  66
#> 830       43H1 2011     23.00000  7.49277451    2011_43H1 niche_data  66
#> 831       37G4 2017     13.00000  7.69943452    2017_37G4   all_data  67
#> 832       39G2 2017     19.00000  7.32303822    2017_39G2   all_data  67
#> 833       39G8 2005     90.00000  4.01203693    2005_39G8   all_data  67
#> 834       40G8 2001     97.98204  2.63913371    2001_40G8   all_data  67
#> 835       43G7 2009     83.80202  2.75113783    2009_43G7   all_data  67
#> 836       37G4 2017     13.00000  7.69943452    2017_37G4 niche_data  67
#> 837       39G2 2017     19.00000  7.32303822    2017_39G2 niche_data  67
#> 838       39G8 2005     88.00000  4.21636343    2005_39G8 niche_data  67
#> 839       40G8 2001     90.00000  3.15584377    2001_40G8 niche_data  67
#> 840       43G7 2009     55.36378  6.83055647    2009_43G7 niche_data  67
#> 841       37G9 2018     43.07566  6.02182955    2018_37G9   all_data  68
#> 842       38G6 2012     26.52553  7.43123707    2012_38G6   all_data  68
#> 843       38G6 2014     26.52553  7.15722446    2014_38G6   all_data  68
#> 844       38G6 2015     26.52553  7.44133138    2015_38G6   all_data  68
#> 845       38G6 2018     26.52553  7.24355191    2018_38G6   all_data  68
#> 846       43G8 1998     27.86322  7.93341895    1998_43G8   all_data  68
#> 847       37G9 2018     43.07566  6.02182955    2018_37G9 niche_data  68
#> 848       38G6 2012     26.52553  7.43123707    2012_38G6 niche_data  68
#> 849       38G6 2014     26.52553  7.15722446    2014_38G6 niche_data  68
#> 850       38G6 2015     26.33665  7.44315857    2015_38G6 niche_data  68
#> 851       38G6 2018     26.52553  7.24355191    2018_38G6 niche_data  68
#> 852       43G8 1998     27.22653  7.97849897    1998_43G8 niche_data  68
#> 853       38G6 2005     26.52553  7.38156144    2005_38G6   all_data  69
#> 854       38G6 2019     26.52553  7.22895141    2019_38G6   all_data  69
#> 855       40G4 2000     39.57072  6.89669517    2000_40G4   all_data  69
#> 856       38G6 2005     26.52553  7.38156144    2005_38G6 niche_data  69
#> 857       38G6 2019     26.52553  7.22895141    2019_38G6 niche_data  69
#> 858       40G4 2000     35.57056  6.99963415    2000_40G4 niche_data  69
#> 859       37G4 2013     13.00000  7.81177253    2013_37G4   all_data  70
#> 860       37G9 2006     43.07566  6.92839127    2006_37G9   all_data  70
#> 861       39G6 2016     68.00000  4.46180697    2016_39G6   all_data  70
#> 862       44H1 2011     30.23161  7.44496383    2011_44H1   all_data  70
#> 863       44H1 2018     30.23161  7.51491885    2018_44H1   all_data  70
#> 864       37G4 2013     13.00000  7.81177253    2013_37G4 niche_data  70
#> 865       37G9 2006     43.07566  6.92839127    2006_37G9 niche_data  70
#> 866       39G6 2016     61.84180  4.99467737    2016_39G6 niche_data  70
#> 867       44H1 2011     30.23161  7.44496383    2011_44H1 niche_data  70
#> 868       44H1 2018     30.23161  7.51491885    2018_44H1 niche_data  70
#> 869       37G3 2006     15.00000  6.77921952    2006_37G3   all_data  71
#> 870       39G6 2015     68.00000  4.69501337    2015_39G6   all_data  71
#> 871       37G3 2006     15.00000  6.77921952    2006_37G3 niche_data  71
#> 872       39G6 2015     60.46079  5.48499764    2015_39G6 niche_data  71
#> 873       43H0 2000    124.04904  2.16185654    2000_43H0   all_data  72
#> 874       43H0 2000     52.76306  7.30636606    2000_43H0 niche_data  72
#> 875       37G9 2013     43.07566  7.19587546    2013_37G9   all_data  73
#> 876       39G6 2012     68.00000  4.63070669    2012_39G6   all_data  73
#> 877       40G4 1994     39.57072  6.64368910    1994_40G4   all_data  73
#> 878       43G7 2001     83.80202  2.22903401    2001_43G7   all_data  73
#> 879       37G9 2013     43.07566  7.19587546    2013_37G9 niche_data  73
#> 880       39G6 2012     62.78092  5.18601194    2012_39G6 niche_data  73
#> 881       40G4 1994     36.54355  7.11109423    1994_40G4 niche_data  73
#> 882       43G7 2001     55.36378  6.02098943    2001_43G7 niche_data  73
#> 883       38G8 2019     40.19787  6.69523960    2019_38G8   all_data  74
#> 884       39G6 2018     68.00000  4.48359910    2018_39G6   all_data  74
#> 885       40G7 2001     38.00000  7.79484072    2001_40G7   all_data  74
#> 886       38G8 2019     28.72128  7.10249326    2019_38G8 niche_data  74
#> 887       39G6 2018     63.86497  4.73929548    2018_39G6 niche_data  74
#> 888       40G7 2001     38.00000  7.79484072    2001_40G7 niche_data  74
#> 889       39G7 2002     56.11417  6.08170908    2002_39G7   all_data  75
#> 890       39H0 2004     28.00000  7.59587762    2004_39H0   all_data  75
#> 891       40G6 2001     50.04572  6.07877011    2001_40G6   all_data  75
#> 892       41G7 2001     38.00000  7.24475804    2001_41G7   all_data  75
#> 893       41G8 2017     96.71104  1.74060096    2017_41G8   all_data  75
#> 894       42H0 2014     57.59084  7.18246174    2014_42H0   all_data  75
#> 895       43H1 1997     23.00000  7.40195341    1997_43H1   all_data  75
#> 896       39G7 2002     52.79956  6.41800974    2002_39G7 niche_data  75
#> 897       39H0 2004     28.00000  7.59587762    2004_39H0 niche_data  75
#> 898       40G6 2001     42.82988  7.30930426    2001_40G6 niche_data  75
#> 899       41G7 2001     38.00000  7.24475804    2001_41G7 niche_data  75
#> 900       41G8 2017     39.00000  5.68935854    2017_41G8 niche_data  75
#> 901       42H0 2014     43.82713  7.57731975    2014_42H0 niche_data  75
#> 902       43H1 1997     23.00000  7.40195341    1997_43H1 niche_data  75
#> 903       37G3 2015     15.00000  7.51025497    2015_37G3   all_data  76
#> 904       38G6 2004     26.52553  7.62649804    2004_38G6   all_data  76
#> 905       40G7 2013     38.00000  7.58829473    2013_40G7   all_data  76
#> 906       43G7 1993     83.80202  5.71966283    1993_43G7   all_data  76
#> 907       37G3 2015     15.00000  7.51025497    2015_37G3 niche_data  76
#> 908       38G6 2004     26.52553  7.62649804    2004_38G6 niche_data  76
#> 909       40G7 2013     38.00000  7.58829473    2013_40G7 niche_data  76
#> 910       43G7 1993     60.79264  6.98859126    1993_43G7 niche_data  76
#> 911       39G3 2019     34.94275  6.35679442    2019_39G3   all_data  77
#> 912       39G8 2008     90.00000  3.55337529    2008_39G8   all_data  77
#> 913       41G7 2002     38.00000  7.76742598    2002_41G7   all_data  77
#> 914       44H1 2010     30.23161  7.80761056    2010_44H1   all_data  77
#> 915       39G3 2019     34.94275  6.35679442    2019_39G3 niche_data  77
#> 916       39G8 2008     88.00000  3.71602300    2008_39G8 niche_data  77
#> 917       41G7 2002     38.00000  7.76742598    2002_41G7 niche_data  77
#> 918       44H1 2010     30.23161  7.80761056    2010_44H1 niche_data  77
#> 919       39G6 2003     68.00000  4.80908935    2003_39G6   all_data  78
#> 920       40G6 2009     50.04572  5.82317698    2009_40G6   all_data  78
#> 921       43G8 1997     27.86322  7.85091087    1997_43G8   all_data  78
#> 922       39G6 2003     66.00000  4.96315876    2003_39G6 niche_data  78
#> 923       40G6 2009     41.90749  7.16388649    2009_40G6 niche_data  78
#> 924       43G8 1997     27.22653  7.86558236    1997_43G8 niche_data  78
#> 925       42G7 2000     79.04017  4.32299305    2000_42G7   all_data  79
#> 926       42G7 2000     73.00000  4.95030390    2000_42G7 niche_data  79
#> 927       37G5 2014     20.00000  7.34042389    2014_37G5   all_data  80
#> 928       39G5 2004     85.00000  0.19579855    2004_39G5   all_data  80
#> 929       43G6 2007     30.91015  7.19362856    2007_43G6   all_data  80
#> 930       43H0 2017    124.04904  2.96888063    2017_43H0   all_data  80
#> 931       37G5 2014     20.00000  7.34042389    2014_37G5 niche_data  80
#> 932       39G5 2004     73.12706  3.33215569    2004_39G5 niche_data  80
#> 933       43G6 2007     30.91015  7.19362856    2007_43G6 niche_data  80
#> 934       43H0 2017     52.76306  7.52792389    2017_43H0 niche_data  80
#> 935       38G2 2006     17.00000  6.50085670    2006_38G2   all_data  81
#> 936       40G8 2016     97.98204  3.64323607    2016_40G8   all_data  81
#> 937       42G6 2006     29.09908  7.24012724    2006_42G6   all_data  81
#> 938       43H0 1998    124.04904  2.63637908    1998_43H0   all_data  81
#> 939       38G2 2006     17.00000  6.50085670    2006_38G2 niche_data  81
#> 940       40G8 2016     90.00000  3.97871353    2016_40G8 niche_data  81
#> 941       42G6 2006     29.09908  7.24012724    2006_42G6 niche_data  81
#> 942       43H0 1998     52.76306  7.38763544    1998_43H0 niche_data  81
#> 943       37G5 2012     20.00000  7.53458289    2012_37G5   all_data  82
#> 944       38G5 2000     61.20802  4.20010455    2000_38G5   all_data  82
#> 945       38G8 2012     40.19787  6.29301979    2012_38G8   all_data  82
#> 946       40G6 1996     50.04572  5.96745477    1996_40G6   all_data  82
#> 947       41G7 2015     38.00000  7.28885181    2015_41G7   all_data  82
#> 948       43H1 2014     23.00000  7.63916607    2014_43H1   all_data  82
#> 949       37G5 2012     20.00000  7.53458289    2012_37G5 niche_data  82
#> 950       38G5 2000     56.90548  4.73024962    2000_38G5 niche_data  82
#> 951       38G8 2012     28.72128  6.71572380    2012_38G8 niche_data  82
#> 952       40G6 1996     41.90749  7.34215376    1996_40G6 niche_data  82
#> 953       41G7 2015     38.00000  7.28885181    2015_41G7 niche_data  82
#> 954       43H1 2014     23.00000  7.63916607    2014_43H1 niche_data  82
#> 955       38G2 2001     17.00000  7.03446366    2001_38G2   all_data  83
#> 956       39G5 2010     85.00000  0.49974598    2010_39G5   all_data  83
#> 957       38G2 2001     17.00000  7.03446366    2001_38G2 niche_data  83
#> 958       39G5 2010     77.00000  2.91743529    2010_39G5 niche_data  83
#> 959       42H0 1997     57.59084  7.32634032    1997_42H0   all_data  84
#> 960       44H1 2014     30.23161  7.69046991    2014_44H1   all_data  84
#> 961       42H0 1997     43.82713  7.45481232    1997_42H0 niche_data  84
#> 962       44H1 2014     30.23161  7.69046991    2014_44H1 niche_data  84
#> 963       38G8 2004     40.19787  7.03729905    2004_38G8   all_data  85
#> 964       41G9 2017    123.90523  2.19198387    2017_41G9   all_data  85
#> 965       43G6 2001     30.91015  7.01162189    2001_43G6   all_data  85
#> 966       38G8 2004     28.72128  7.24232911    2004_38G8 niche_data  85
#> 967       41G9 2017     59.00000  6.45921651    2017_41G9 niche_data  85
#> 968       43G6 2001     30.91015  7.01162189    2001_43G6 niche_data  85
#> 969       39G5 2017     85.00000 -0.12077656    2017_39G5   all_data  86
#> 970       40G7 2016     38.00000  7.36355539    2016_40G7   all_data  86
#> 971       41G7 1995     38.00000  7.39917606    1995_41G7   all_data  86
#> 972       41G8 2009     96.71104  0.74774169    2009_41G8   all_data  86
#> 973       39G5 2017     71.00000  3.28038893    2017_39G5 niche_data  86
#> 974       40G7 2016     38.00000  7.36355539    2016_40G7 niche_data  86
#> 975       41G7 1995     38.00000  7.39917606    1995_41G7 niche_data  86
#> 976       41G8 2009     36.42837  6.92409437    2009_41G8 niche_data  86
#> 977       39G3 2004     34.94275  5.27309519    2004_39G3   all_data  87
#> 978       39G5 2019     85.00000 -0.23527687    2019_39G5   all_data  87
#> 979       43H1 2015     23.00000  7.54026187    2015_43H1   all_data  87
#> 980       43H1 2019     23.00000  7.44360089    2019_43H1   all_data  87
#> 981       39G3 2004     34.94275  5.27309519    2004_39G3 niche_data  87
#> 982       39G5 2019     72.56353  3.83140179    2019_39G5 niche_data  87
#> 983       43H1 2015     23.00000  7.54026187    2015_43H1 niche_data  87
#> 984       43H1 2019     23.00000  7.44360089    2019_43H1 niche_data  87
#> 985       40G7 1999     38.00000  7.74492186    1999_40G7   all_data  88
#> 986       40G7 1999     38.00000  7.74492186    1999_40G7 niche_data  88
#> 987       41G7 2004     38.00000  7.20533952    2004_41G7   all_data  89
#> 988       41G7 2004     38.00000  7.20533952    2004_41G7 niche_data  89
#> 989       38G5 2008     61.20802  3.58811619    2008_38G5   all_data  90
#> 990       38G5 2009     61.20802  4.35012814    2009_38G5   all_data  90
#> 991       38G6 2010     26.52553  7.73716256    2010_38G6   all_data  90
#> 992       38G7 2010     21.00000  7.77165722    2010_38G7   all_data  90
#> 993       38G5 2008     58.73682  3.84292777    2008_38G5 niche_data  90
#> 994       38G5 2009     61.00000  4.35664393    2009_38G5 niche_data  90
#> 995       38G6 2010     26.52553  7.73716256    2010_38G6 niche_data  90
#> 996       38G7 2010     21.00000  7.77165722    2010_38G7 niche_data  90
#> 997       42H0 2015     57.59084  6.81596821    2015_42H0   all_data  91
#> 998       43G7 1996     83.80202  2.28168091    1996_43G7   all_data  91
#> 999       43G8 1999     27.86322  7.72537347    1999_43G8   all_data  91
#> 1000      42H0 2015     43.82713  7.44880858    2015_42H0 niche_data  91
#> 1001      43G7 1996     55.86736  5.47881745    1996_43G7 niche_data  91
#> 1002      43G8 1999     27.22653  7.74512973    1999_43G8 niche_data  91
#> 1003      38G6 2013     26.52553  7.60019626    2013_38G6   all_data  92
#> 1004      41G7 1999     38.00000  7.82929834    1999_41G7   all_data  92
#> 1005      38G6 2013     26.52553  7.60019626    2013_38G6 niche_data  92
#> 1006      41G7 1999     38.00000  7.82929834    1999_41G7 niche_data  92
#> 1007      39G4 2001     40.00000  4.46305626    2001_39G4   all_data  93
#> 1008      40G8 2005     97.98204  2.59568011    2005_40G8   all_data  93
#> 1009      39G4 2001     40.00000  4.46305626    2001_39G4 niche_data  93
#> 1010      40G8 2005     90.00000  3.28063415    2005_40G8 niche_data  93
#> 1011      38G2 2018     17.00000  7.05406330    2018_38G2   all_data  94
#> 1012      38G2 2018     17.00000  7.05406330    2018_38G2 niche_data  94
#> 1013      40G7 2017     38.00000  7.38078810    2017_40G7   all_data  95
#> 1014      40G7 2017     38.00000  7.38078810    2017_40G7 niche_data  95
#> 1015      39G3 2015     34.94275  5.64237898    2015_39G3   all_data  96
#> 1016      41G8 2004     96.71104  1.63543829    2004_41G8   all_data  96
#> 1017      39G3 2015     34.94275  5.64237898    2015_39G3 niche_data  96
#> 1018      41G8 2004     39.00000  7.40887404    2004_41G8 niche_data  96
#> 1019      39G3 2011     34.94275  5.43946623    2011_39G3   all_data  97
#> 1020      39G6 2013     68.00000  5.55296714    2013_39G6   all_data  97
#> 1021      39G3 2011     34.94275  5.43946623    2011_39G3 niche_data  97
#> 1022      39G6 2013     63.45345  5.88223572    2013_39G6 niche_data  97
#> 1023      40G9 2005     83.07490  3.79581173    2005_40G9   all_data  98
#> 1024      41G9 2014    123.90523  4.29317918    2014_41G9   all_data  98
#> 1025      43H1 2006     23.00000  7.48197764    2006_43H1   all_data  98
#> 1026      40G9 2005     80.96977  3.88507109    2005_40G9 niche_data  98
#> 1027      41G9 2014     59.00000  6.44070092    2014_41G9 niche_data  98
#> 1028      43H1 2006     23.00000  7.48197764    2006_43H1 niche_data  98
#> 1029      38G6 2011     26.52553  7.41450390    2011_38G6   all_data  99
#> 1030      41G9 2018    123.90523  1.39367517    2018_41G9   all_data  99
#> 1031      38G6 2011     26.52553  7.41450390    2011_38G6 niche_data  99
#> 1032      41G9 2018     59.00000  5.26419104    2018_41G9 niche_data  99
#> 1033      40G4 2004     39.57072  7.49167487    2004_40G4   all_data 100
#> 1034      40G5 1993     62.00000  5.84951399    1993_40G5   all_data 100
#> 1035      40G8 2008     97.98204  2.43131338    2008_40G8   all_data 100
#> 1036      44H1 2012     30.23161  7.51433724    2012_44H1   all_data 100
#> 1037      40G4 2004     36.54355  7.66457818    2004_40G4 niche_data 100
#> 1038      40G5 1993     62.00000  5.84951399    1993_40G5 niche_data 100
#> 1039      40G8 2008     90.00000  3.09275786    2008_40G8 niche_data 100
#> 1040      44H1 2012     30.23161  7.51433724    2012_44H1 niche_data 100
#> 1041      39G6 2011     68.00000  5.29328079    2011_39G6   all_data 101
#> 1042      41G7 2012     38.00000  6.60521801    2012_41G7   all_data 101
#> 1043      39G6 2011     63.45345  5.49532487    2011_39G6 niche_data 101
#> 1044      41G7 2012     38.00000  6.60521801    2012_41G7 niche_data 101
#> 1045      37G2 2005     14.28574  5.90627463    2005_37G2   all_data 102
#> 1046      37G2 2005     14.28574  5.90627463    2005_37G2 niche_data 102
#> 1047      37G9 2010     43.07566  7.06295231    2010_37G9   all_data 103
#> 1048      38G8 2009     40.19787  6.48812840    2009_38G8   all_data 103
#> 1049      39G7 2015     56.11417  6.35096228    2015_39G7   all_data 103
#> 1050      37G9 2010     43.07566  7.06295231    2010_37G9 niche_data 103
#> 1051      38G8 2009     28.72128  6.83011914    2009_38G8 niche_data 103
#> 1052      39G7 2015     52.79956  6.68180737    2015_39G7 niche_data 103
#> 1053      40G5 1996     62.00000  3.98640098    1996_40G5   all_data 104
#> 1054      43G9 1995    180.66532  0.90038755    1995_43G9   all_data 104
#> 1055      40G5 1996     51.23014  6.63837867    1996_40G5 niche_data 104
#> 1056      43G9 1995     66.36603  6.63403758    1995_43G9 niche_data 104
#> 1057      38G9 2011     91.00641  2.66904059    2011_38G9   all_data 105
#> 1058      39G4 2002     40.00000  4.19481062    2002_39G4   all_data 105
#> 1059      39G7 2012     56.11417  6.47037009    2012_39G7   all_data 105
#> 1060      41G7 2009     38.00000  6.88064539    2009_41G7   all_data 105
#> 1061      42H0 2010     57.59084  6.99162886    2010_42H0   all_data 105
#> 1062      42H0 2017     57.59084  7.31655145    2017_42H0   all_data 105
#> 1063      38G9 2011     61.59609  5.86801592    2011_38G9 niche_data 105
#> 1064      39G4 2002     40.00000  4.40569062    2002_39G4 niche_data 105
#> 1065      39G7 2012     52.79956  6.69832974    2012_39G7 niche_data 105
#> 1066      41G7 2009     38.00000  6.88064539    2009_41G7 niche_data 105
#> 1067      42H0 2010     43.82713  7.70098386    2010_42H0 niche_data 105
#> 1068      42H0 2017     43.82713  7.47738319    2017_42H0 niche_data 105
#> 1069      39G4 2018     40.00000  4.21047297    2018_39G4   all_data 106
#> 1070      39G4 2018     40.00000  4.23494449    2018_39G4 niche_data 106
#> 1071      37G9 2004     43.07566  7.10166309    2004_37G9   all_data 107
#> 1072      41G7 2017     38.00000  6.21160454    2017_41G7   all_data 107
#> 1073      43H0 2014    124.04904  4.75415044    2014_43H0   all_data 107
#> 1074      37G9 2004     43.07566  7.10166309    2004_37G9 niche_data 107
#> 1075      41G7 2017     38.00000  6.21160454    2017_41G7 niche_data 107
#> 1076      43H0 2014     52.76306  7.57750290    2014_43H0 niche_data 107
#> 1077      39G3 1999     34.94275  4.91017197    1999_39G3   all_data 108
#> 1078      39G5 2006     85.00000  0.66407020    2006_39G5   all_data 108
#> 1079      39G5 2007     85.00000 -0.19914700    2007_39G5   all_data 108
#> 1080      39G3 1999     34.94275  4.91017197    1999_39G3 niche_data 108
#> 1081      39G5 2006     76.51884  3.12887255    2006_39G5 niche_data 108
#> 1082      39G5 2007     71.00000  3.32024710    2007_39G5 niche_data 108
#> 1083      40G5 2008     62.00000  4.39431826    2008_40G5   all_data 109
#> 1084      40G7 2003     38.00000  7.66182160    2003_40G7   all_data 109
#> 1085      40G5 2008     52.40050  6.44442586    2008_40G5 niche_data 109
#> 1086      40G7 2003     38.00000  7.66182160    2003_40G7 niche_data 109
#> 1087      37G5 2011     20.00000  7.47119644    2011_37G5   all_data 110
#> 1088      40G5 2018     62.00000  4.48231853    2018_40G5   all_data 110
#> 1089      42H0 2002     57.59084  7.67302500    2002_42H0   all_data 110
#> 1090      37G5 2011     20.00000  7.47119644    2011_37G5 niche_data 110
#> 1091      40G5 2018     52.13788  6.71767022    2018_40G5 niche_data 110
#> 1092      42H0 2002     43.82713  7.92063266    2002_42H0 niche_data 110
#> 1093      37G5 2015     20.00000  7.57599008    2015_37G5   all_data 111
#> 1094      37G5 2015     20.00000  7.57599008    2015_37G5 niche_data 111
#> 1095      43G8 2000     27.86322  7.52522080    2000_43G8   all_data 112
#> 1096      43G8 2000     27.22653  7.55683622    2000_43G8 niche_data 112
#> 1097      38G2 2012     17.00000  7.34772603    2012_38G2   all_data 113
#> 1098      41G7 2011     38.00000  6.99137124    2011_41G7   all_data 113
#> 1099      42H0 2018     57.59084  4.89771773    2018_42H0   all_data 113
#> 1100      38G2 2012     17.00000  7.34772603    2012_38G2 niche_data 113
#> 1101      41G7 2011     38.00000  6.99137124    2011_41G7 niche_data 113
#> 1102      42H0 2018     43.63243  7.16952799    2018_42H0 niche_data 113
#> 1103      39G7 2010     56.11417  5.78922058    2010_39G7   all_data 114
#> 1104      39H0 2015     28.00000  7.34469546    2015_39H0   all_data 114
#> 1105      40G5 2019     62.00000  5.96321726    2019_40G5   all_data 114
#> 1106      41G8 1998     96.71104  2.01591342    1998_41G8   all_data 114
#> 1107      39G7 2010     52.79956  5.86896845    2010_39G7 niche_data 114
#> 1108      39H0 2015     28.00000  7.34469546    2015_39H0 niche_data 114
#> 1109      40G5 2019     54.11709  6.82627094    2019_40G5 niche_data 114
#> 1110      41G8 1998     39.02779  7.22169134    1998_41G8 niche_data 114
#> 1111      38G8 2014     40.19787  6.60425340    2014_38G8   all_data 115
#> 1112      38G8 2014     28.72128  6.95664512    2014_38G8 niche_data 115
#> 1113      38G2 2005     17.00000  6.52974683    2005_38G2   all_data 116
#> 1114      39G4 1993     40.00000  4.35494662    1993_39G4   all_data 116
#> 1115      43H0 2010    124.04904  1.59579190    2010_43H0   all_data 116
#> 1116      44G9 2013     98.95766  1.12771501    2013_44G9   all_data 116
#> 1117      38G2 2005     17.00000  6.52974683    2005_38G2 niche_data 116
#> 1118      39G4 1993     40.00000  4.35494662    1993_39G4 niche_data 116
#> 1119      43H0 2010     52.76306  7.41415173    2010_43H0 niche_data 116
#> 1120      44G9 2013     44.20763  7.49682566    2013_44G9 niche_data 116
#> 1121      38G7 2007     21.00000  7.50177016    2007_38G7   all_data 117
#> 1122      40G4 1997     39.57072  7.67631204    1997_40G4   all_data 117
#> 1123      38G7 2007     21.00000  7.50177016    2007_38G7 niche_data 117
#> 1124      40G4 1997     36.50172  7.73018194    1997_40G4 niche_data 117
#> 1125      38G8 2017     40.19787  7.05755362    2017_38G8   all_data 118
#> 1126      38G8 2017     28.72128  7.34795764    2017_38G8 niche_data 118
#> 1127      38G2 2009     17.00000  6.89082645    2009_38G2   all_data 119
#> 1128      38G5 2011     61.20802  4.15324823    2011_38G5   all_data 119
#> 1129      38G2 2009     17.00000  6.89082645    2009_38G2 niche_data 119
#> 1130      38G5 2011     60.00000  4.24229279    2011_38G5 niche_data 119
#> 1131      39G4 1995     40.00000  3.94518620    1995_39G4   all_data 120
#> 1132      39G4 2013     40.00000  4.25383159    2013_39G4   all_data 120
#> 1133      41G7 2003     38.00000  7.72017146    2003_41G7   all_data 120
#> 1134      41G8 2008     96.71104  1.25173078    2008_41G8   all_data 120
#> 1135      41G9 2006    123.90523  4.00397543    2006_41G9   all_data 120
#> 1136      39G4 1995     40.00000  4.02387353    1995_39G4 niche_data 120
#> 1137      39G4 2013     40.00000  4.27054090    2013_39G4 niche_data 120
#> 1138      41G7 2003     38.00000  7.72017146    2003_41G7 niche_data 120
#> 1139      41G8 2008     37.28142  7.16764713    2008_41G8 niche_data 120
#> 1140      41G9 2006     59.00000  6.55400060    2006_41G9 niche_data 120
#> 1141      40G5 2004     62.00000  3.77095732    2004_40G5   all_data 121
#> 1142      41G8 1994     96.71104  3.86400657    1994_41G8   all_data 121
#> 1143      40G5 2004     53.95056  5.53068901    2004_40G5 niche_data 121
#> 1144      41G8 1994     39.15152  7.96269881    1994_41G8 niche_data 121
#> 1145      38G6 2006     26.52553  7.42651795    2006_38G6   all_data 123
#> 1146      40G9 1996     83.07490  4.83088813    1996_40G9   all_data 123
#> 1147      43G7 2002     83.80202  3.47556738    2002_43G7   all_data 123
#> 1148      38G6 2006     26.52553  7.42651795    2006_38G6 niche_data 123
#> 1149      40G9 1996     82.00000  4.90157628    1996_40G9 niche_data 123
#> 1150      43G7 2002     56.09113  7.16770256    2002_43G7 niche_data 123
#> 1151      38G4 1999     25.69453  6.61493246    1999_38G4   all_data 124
#> 1152      40G4 2017     39.57072  6.10119260    2017_40G4   all_data 124
#> 1153      38G4 1999     25.69453  6.61493246    1999_38G4 niche_data 124
#> 1154      40G4 2017     37.24826  6.42609647    2017_40G4 niche_data 124
#> 1155      38G4 2007     25.69453  6.86046332    2007_38G4   all_data 125
#> 1156      41G6 2009     32.27464  7.58142742    2009_41G6   all_data 125
#> 1157      42H0 2019     57.59084  6.77280030    2019_42H0   all_data 125
#> 1158      38G4 2007     25.69453  6.86046332    2007_38G4 niche_data 125
#> 1159      41G6 2009     32.27464  7.58142742    2009_41G6 niche_data 125
#> 1160      42H0 2019     43.82713  7.34102286    2019_42H0 niche_data 125
#> 1161      40G4 2011     39.57072  5.75903366    2011_40G4   all_data 126
#> 1162      43G8 2006     27.86322  7.30374338    2006_43G8   all_data 126
#> 1163      40G4 2011     39.57072  5.75903366    2011_40G4 niche_data 126
#> 1164      43G8 2006     27.20216  7.32275188    2006_43G8 niche_data 126
#> 1165      40G5 2006     62.00000  4.29737070    2006_40G5   all_data 127
#> 1166      40G5 2006     52.95001  5.91394436    2006_40G5 niche_data 127
#> 1167      39G4 2014     40.00000  4.65658249    2014_39G4   all_data 128
#> 1168      39G4 2014     40.00000  4.66310234    2014_39G4 niche_data 128
#> 1169      38G8 2016     40.19787  7.11249324    2016_38G8   all_data 129
#> 1170      39G7 2003     56.11417  6.52158498    2003_39G7   all_data 129
#> 1171      40G6 2011     50.04572  5.13389926    2011_40G6   all_data 129
#> 1172      40G9 1997     83.07490  3.10511344    1997_40G9   all_data 129
#> 1173      42H0 2008     57.59084  7.51650032    2008_42H0   all_data 129
#> 1174      38G8 2016     28.72128  7.39316880    2016_38G8 niche_data 129
#> 1175      39G7 2003     52.79956  6.65637766    2003_39G7 niche_data 129
#> 1176      40G6 2011     49.47801  5.16925400    2011_40G6 niche_data 129
#> 1177      40G9 1997     78.99048  3.76242678    1997_40G9 niche_data 129
#> 1178      42H0 2008     43.63243  7.63218397    2008_42H0 niche_data 129
#> 1179      40G4 2001     39.57072  7.01689228    2001_40G4   all_data 130
#> 1180      41G8 2015     96.71104  1.55584547    2015_41G8   all_data 130
#> 1181      40G4 2001     39.36247  7.06737772    2001_40G4 niche_data 130
#> 1182      41G8 2015     36.99837  7.52521165    2015_41G8 niche_data 130
#> 1183      39G9 2011     99.00000  1.84474588    2011_39G9   all_data 131
#> 1184      43G8 2007     27.86322  7.58013457    2007_43G8   all_data 131
#> 1185      39G9 2011     83.00000  3.16286539    2011_39G9 niche_data 131
#> 1186      43G8 2007     27.22653  7.67584710    2007_43G8 niche_data 131
#> 1187      39G4 2010     40.00000  4.86560762    2010_39G4   all_data 133
#> 1188      41G6 2008     32.27464  7.22072042    2008_41G6   all_data 133
#> 1189      39G4 2010     40.00000  4.89817211    2010_39G4 niche_data 133
#> 1190      41G6 2008     32.27464  7.22072042    2008_41G6 niche_data 133
#> 1191      41G8 2013     96.71104  1.63166389    2013_41G8   all_data 134
#> 1192      41G8 2013     38.46899  7.62869595    2013_41G8 niche_data 134
#> 1193      43G8 1995     27.86322  7.78370400    1995_43G8   all_data 135
#> 1194      43G8 1995     27.22653  7.79432599    1995_43G8 niche_data 135
#> 1195      39G6 2019     68.00000  4.92685941    2019_39G6   all_data 136
#> 1196      41G8 2003     96.71104  2.28748458    2003_41G8   all_data 136
#> 1197      39G6 2019     61.18668  5.49507827    2019_39G6 niche_data 136
#> 1198      41G8 2003     39.02779  7.88466493    2003_41G8 niche_data 136
#> 1199      40G4 2012     39.57072  6.77998314    2012_40G4   all_data 137
#> 1200      40G4 2012     36.50172  6.94373407    2012_40G4 niche_data 137
#> 1201      40G9 2008     83.07490  3.28043858    2008_40G9   all_data 138
#> 1202      41G9 2000    123.90523  2.29416736    2000_41G9   all_data 138
#> 1203      40G9 2008     79.08962  3.69890131    2008_40G9 niche_data 138
#> 1204      41G9 2000     59.00000  6.90356030    2000_41G9 niche_data 138
#> 1205      38G2 2007     17.00000  7.00802368    2007_38G2   all_data 140
#> 1206      39G5 2018     85.00000 -0.94059329    2018_39G5   all_data 140
#> 1207      40G7 1996     38.00000  7.74572619    1996_40G7   all_data 140
#> 1208      38G2 2007     17.00000  7.00802368    2007_38G2 niche_data 140
#> 1209      39G5 2018     74.64137  3.54285812    2018_39G5 niche_data 140
#> 1210      40G7 1996     38.00000  7.74572619    1996_40G7 niche_data 140
#> 1211      38G4 1993     25.69453  7.44351707    1993_38G4   all_data 141
#> 1212      40G4 1998     39.57072  7.73346132    1998_40G4   all_data 141
#> 1213      40G8 2006     97.98204  2.88562391    2006_40G8   all_data 141
#> 1214      40H0 2019     44.00000  7.16482277    2019_40H0   all_data 141
#> 1215      38G4 1993     25.69453  7.44351707    1993_38G4 niche_data 141
#> 1216      40G4 1998     37.64887  7.76132956    1998_40G4 niche_data 141
#> 1217      40G8 2006     90.00000  3.45973562    2006_40G8 niche_data 141
#> 1218      40H0 2019     44.00000  7.16482277    2019_40H0 niche_data 141
#> 1219      39G3 1994     34.94275  5.66032144    1994_39G3   all_data 142
#> 1220      39G3 1994     34.94275  5.66032144    1994_39G3 niche_data 142
#> 1221      39G3 2013     34.94275  5.32856627    2013_39G3   all_data 143
#> 1222      43H1 2012     23.00000  7.52534744    2012_43H1   all_data 143
#> 1223      39G3 2013     34.94275  5.32856627    2013_39G3 niche_data 143
#> 1224      43H1 2012     23.00000  7.52534744    2012_43H1 niche_data 143
#> 1225      38G5 2013     61.20802  5.55774045    2013_38G5   all_data 145
#> 1226      39H0 2019     28.00000  7.27158080    2019_39H0   all_data 145
#> 1227      38G5 2013     60.00000  5.65704849    2013_38G5 niche_data 145
#> 1228      39H0 2019     28.00000  7.27158080    2019_39H0 niche_data 145
#> 1229      38G4 2013     25.69453  7.23674366    2013_38G4   all_data 146
#> 1230      40G6 2015     50.04572  6.63725396    2015_40G6   all_data 146
#> 1231      41G9 2008    123.90523  2.02846195    2008_41G9   all_data 146
#> 1232      38G4 2013     25.69453  7.23674366    2013_38G4 niche_data 146
#> 1233      40G6 2015     49.82187  6.66411754    2015_40G6 niche_data 146
#> 1234      41G9 2008     59.00000  7.30938149    2008_41G9 niche_data 146
#> 1235      40G7 2002     38.00000  7.76402980    2002_40G7   all_data 147
#> 1236      40G7 2002     38.00000  7.76402980    2002_40G7 niche_data 147
#> 1237      38G4 2000     25.69453  6.62727143    2000_38G4   all_data 148
#> 1238      38G4 2000     25.69453  6.62727143    2000_38G4 niche_data 148
#> 1239      38G5 2014     61.20802  4.44543129    2014_38G5   all_data 149
#> 1240      38G5 2014     60.00000  4.54749911    2014_38G5 niche_data 149
#> 1241      40G7 1994     38.00000  7.75987247    1994_40G7   all_data 151
#> 1242      40G7 2014     38.00000  7.25615583    2014_40G7   all_data 151
#> 1243      40G7 1994     38.00000  7.75987247    1994_40G7 niche_data 151
#> 1244      40G7 2014     38.00000  7.25615583    2014_40G7 niche_data 151
#> 1245      40G9 2017     83.07490  3.59772666    2017_40G9   all_data 152
#> 1246      40G9 2017     82.00000  3.77035548    2017_40G9 niche_data 152
#> 1247      38G5 2005     61.20802  4.15142679    2005_38G5   all_data 153
#> 1248      39G4 2009     40.00000  4.44222779    2009_39G4   all_data 153
#> 1249      40G4 1995     39.57072  6.82733389    1995_40G4   all_data 153
#> 1250      43G8 2008     27.86322  7.62677492    2008_43G8   all_data 153
#> 1251      38G5 2005     58.14054  4.33857556    2005_38G5 niche_data 153
#> 1252      39G4 2009     40.00000  4.46090773    2009_39G4 niche_data 153
#> 1253      40G4 1995     36.69833  6.94459287    1995_40G4 niche_data 153
#> 1254      43G8 2008     27.22653  7.64812681    2008_43G8 niche_data 153
#> 1255      38G2 2019     17.00000  7.01917332    2019_38G2   all_data 154
#> 1256      39G3 1996     34.94275  5.08413770    1996_39G3   all_data 154
#> 1257      40G6 2018     50.04572  6.88833259    2018_40G6   all_data 154
#> 1258      40G7 1995     38.00000  7.53081298    1995_40G7   all_data 154
#> 1259      38G2 2019     17.00000  7.01917332    2019_38G2 niche_data 154
#> 1260      39G3 1996     34.94275  5.08413770    1996_39G3 niche_data 154
#> 1261      40G6 2018     49.82187  7.12124209    2018_40G6 niche_data 154
#> 1262      40G7 1995     38.00000  7.53081298    1995_40G7 niche_data 154
#> 1263      41G6 2006     32.27464  7.21604690    2006_41G6   all_data 155
#> 1264      44H1 2008     30.23161  7.62696403    2008_44H1   all_data 155
#> 1265      41G6 2006     32.27464  7.21604690    2006_41G6 niche_data 155
#> 1266      44H1 2008     30.23161  7.62696403    2008_44H1 niche_data 155
#> 1267      39G3 2016     34.94275  5.77951450    2016_39G3   all_data 156
#> 1268      39G3 2016     34.94275  5.77951450    2016_39G3 niche_data 156
#> 1269      42H0 2000     57.59084  6.98820980    2000_42H0   all_data 158
#> 1270      42H0 2000     43.63243  7.45734973    2000_42H0 niche_data 158
#> 1271      38G5 2006     61.20802  4.52116411    2006_38G5   all_data 159
#> 1272      38G5 2006     61.00000  4.52658296    2006_38G5 niche_data 159
#> 1273      40G6 2019     50.04572  6.66125063    2019_40G6   all_data 160
#> 1274      40G9 2010     83.07490  2.75766753    2010_40G9   all_data 160
#> 1275      40G6 2019     49.82187  6.73539189    2019_40G6 niche_data 160
#> 1276      40G9 2010     80.54233  2.99374335    2010_40G9 niche_data 160
#> 1277      38G8 2011     40.19787  6.30268575    2011_38G8   all_data 161
#> 1278      38G8 2011     28.72128  6.77525443    2011_38G8 niche_data 161
#> 1279      37G5 2008     20.00000  7.54612462    2008_37G5   all_data 162
#> 1280      38G8 2015     40.19787  7.00049007    2015_38G8   all_data 162
#> 1281      39G4 1994     40.00000  3.92351235    1994_39G4   all_data 162
#> 1282      39G4 1997     40.00000  3.55661249    1997_39G4   all_data 162
#> 1283      41G8 2014     96.71104  2.26527615    2014_41G8   all_data 162
#> 1284      37G5 2008     20.00000  7.54612462    2008_37G5 niche_data 162
#> 1285      38G8 2015     28.72128  7.22503949    2015_38G8 niche_data 162
#> 1286      39G4 1994     40.00000  3.93069289    1994_39G4 niche_data 162
#> 1287      39G4 1997     38.53589  3.99912509    1997_39G4 niche_data 162
#> 1288      41G8 2014     38.87665  7.39377160    2014_41G8 niche_data 162
#> 1289      41G7 2007     38.00000  6.98584873    2007_41G7   all_data 163
#> 1290      44H1 2013     30.23161  7.85697799    2013_44H1   all_data 163
#> 1291      41G7 2007     38.00000  6.98584873    2007_41G7 niche_data 163
#> 1292      44H1 2013     30.23161  7.85697799    2013_44H1 niche_data 163
#> 1293      39G6 2014     68.00000  4.48321692    2014_39G6   all_data 164
#> 1294      39G6 2014     64.61201  5.06094493    2014_39G6 niche_data 164
#> 1295      41G8 1995     96.71104  3.77008160    1995_41G8   all_data 165
#> 1296      41G8 2016     96.71104  2.47152388    2016_41G8   all_data 165
#> 1297      41G8 1995     39.15152  7.69411671    1995_41G8 niche_data 165
#> 1298      41G8 2016     39.00000  7.00326401    2016_41G8 niche_data 165
#> 1299      38G4 2003     25.69453  6.86679905    2003_38G4   all_data 166
#> 1300      40G5 2011     62.00000  4.63934476    2011_40G5   all_data 166
#> 1301      38G4 2003     25.69453  6.86679905    2003_38G4 niche_data 166
#> 1302      40G5 2011     54.04199  4.95768766    2011_40G5 niche_data 166
#> 1303      39G3 2012     34.94275  5.36349430    2012_39G3   all_data 167
#> 1304      40G7 1997     38.00000  7.63254954    1997_40G7   all_data 167
#> 1305      42H0 2006     57.59084  6.93334609    2006_42H0   all_data 167
#> 1306      39G3 2012     34.94275  5.36349430    2012_39G3 niche_data 167
#> 1307      40G7 1997     38.00000  7.63254954    1997_40G7 niche_data 167
#> 1308      42H0 2006     43.82713  7.47710816    2006_42H0 niche_data 167
#> 1309      38G9 2017     91.00641  5.72120165    2017_38G9   all_data 168
#> 1310      39G6 2008     68.00000  3.56122781    2008_39G6   all_data 168
#> 1311      38G9 2017     61.59609  6.79435973    2017_38G9 niche_data 168
#> 1312      39G6 2008     60.23183  4.35002074    2008_39G6 niche_data 168
#> 1313      38G8 2005     40.19787  6.84171114    2005_38G8   all_data 170
#> 1314      41G7 2008     38.00000  6.43876052    2008_41G7   all_data 170
#> 1315      38G8 2005     28.72128  7.06736658    2005_38G8 niche_data 170
#> 1316      41G7 2008     38.00000  6.43876052    2008_41G7 niche_data 170
#> 1317      38G4 2019     25.69453  6.77384764    2019_38G4   all_data 171
#> 1318      39G6 2006     68.00000  3.98697288    2006_39G6   all_data 171
#> 1319      40G7 2015     38.00000  7.31653944    2015_40G7   all_data 171
#> 1320      38G4 2019     25.69453  6.77384764    2019_38G4 niche_data 171
#> 1321      39G6 2006     62.00000  4.48289550    2006_39G6 niche_data 171
#> 1322      40G7 2015     38.00000  7.31653944    2015_40G7 niche_data 171
#> 1323      39G7 2005     56.11417  6.11448325    2005_39G7   all_data 172
#> 1324      42H0 2003     57.59084  6.46219108    2003_42H0   all_data 172
#> 1325      39G7 2005     52.79956  6.36177192    2005_39G7 niche_data 172
#> 1326      42H0 2003     43.82713  7.26433948    2003_42H0 niche_data 172
#> 1327      38G4 2009     25.69453  6.77889825    2009_38G4   all_data 173
#> 1328      40G7 2009     38.00000  7.10820713    2009_40G7   all_data 173
#> 1329      43H1 2013     23.00000  7.75528830    2013_43H1   all_data 173
#> 1330      38G4 2009     25.69453  6.77889825    2009_38G4 niche_data 173
#> 1331      40G7 2009     38.00000  7.10820713    2009_40G7 niche_data 173
#> 1332      43H1 2013     23.00000  7.75528830    2013_43H1 niche_data 173
#> 1333      38G2 2003     17.00000  6.83482690    2003_38G2   all_data 174
#> 1334      38G8 2008     40.19787  6.66721958    2008_38G8   all_data 174
#> 1335      38G9 2003     91.00641  5.05448490    2003_38G9   all_data 174
#> 1336      40G4 2013     39.57072  6.65463491    2013_40G4   all_data 174
#> 1337      38G2 2003     17.00000  6.83482690    2003_38G2 niche_data 174
#> 1338      38G8 2008     28.72128  6.84890074    2008_38G8 niche_data 174
#> 1339      38G9 2003     61.59609  6.26390020    2003_38G9 niche_data 174
#> 1340      40G4 2013     36.54355  6.80731035    2013_40G4 niche_data 174
#> 1341      38G4 2001     25.69453  6.71510575    2001_38G4   all_data 175
#> 1342      38G4 2001     25.69453  6.71510575    2001_38G4 niche_data 175
#> 1343      41G7 2006     38.00000  7.27619990    2006_41G7   all_data 176
#> 1344      41G7 2006     38.00000  7.27619990    2006_41G7 niche_data 176
#> 1345      38G4 1994     25.69453  6.80189280    1994_38G4   all_data 178
#> 1346      38G4 1994     25.69453  6.80189280    1994_38G4 niche_data 178
#> 1347      38G9 2008     91.00641  3.64833953    2008_38G9   all_data 179
#> 1348      39G7 2008     56.11417  5.70595126    2008_39G7   all_data 179
#> 1349      38G9 2008     61.59609  6.50542064    2008_38G9 niche_data 179
#> 1350      39G7 2008     52.79956  6.20496261    2008_39G7 niche_data 179
#> 1351      40G4 2015     39.57072  6.18746752    2015_40G4   all_data 180
#> 1352      40G6 1998     50.04572  5.19966916    1998_40G6   all_data 180
#> 1353      40G4 2015     35.44424  6.62715381    2015_40G4 niche_data 180
#> 1354      40G6 1998     46.02198  6.22255972    1998_40G6 niche_data 180
#> 1355      37G5 2009     20.00000  7.53478008    2009_37G5   all_data 182
#> 1356      39G7 2009     56.11417  5.56207695    2009_39G7   all_data 182
#> 1357      37G5 2009     20.00000  7.53478008    2009_37G5 niche_data 182
#> 1358      39G7 2009     52.79956  5.84652604    2009_39G7 niche_data 182
#> 1359      39G3 1997     34.94275  6.27479508    1997_39G3   all_data 183
#> 1360      40G9 2016     83.07490  3.83246903    2016_40G9   all_data 183
#> 1361      39G3 1997     34.00000  6.53907745    1997_39G3 niche_data 183
#> 1362      40G9 2016     81.00000  3.89939528    2016_40G9 niche_data 183
#> 1363      39G4 2000     40.00000  3.20654879    2000_39G4   all_data 184
#> 1364      40G4 2019     39.57072  7.39894685    2019_40G4   all_data 184
#> 1365      40G5 1997     62.00000  3.13611007    1997_40G5   all_data 184
#> 1366      39G4 2000     38.05466  3.57805384    2000_39G4 niche_data 184
#> 1367      40G4 2019     39.17228  7.45431028    2019_40G4 niche_data 184
#> 1368      40G5 1997     50.22424  7.22362839    1997_40G5 niche_data 184
#> 1369      38G4 2006     25.69453  6.10632164    2006_38G4   all_data 185
#> 1370      40G5 2013     62.00000  4.19855410    2013_40G5   all_data 185
#> 1371      40G8 2007     97.98204  1.79331435    2007_40G8   all_data 185
#> 1372      41H0 2011     28.04549  7.46107212    2011_41H0   all_data 185
#> 1373      42H0 2007     57.59084  7.03798246    2007_42H0   all_data 185
#> 1374      38G4 2006     25.42107  6.11445611    2006_38G4 niche_data 185
#> 1375      40G5 2013     58.07738  4.91823161    2013_40G5 niche_data 185
#> 1376      40G8 2007     90.00000  2.69466607    2007_40G8 niche_data 185
#> 1377      41H0 2011     28.00000  7.46175556    2011_41H0 niche_data 185
#> 1378      42H0 2007     43.63243  7.45816172    2007_42H0 niche_data 185
#> 1379      39G4 2019     40.00000  5.40967965    2019_39G4   all_data 187
#> 1380      39G4 2019     40.00000  5.41843341    2019_39G4 niche_data 187
#> 1381      40G5 1998     62.00000  4.24775974    1998_40G5   all_data 189
#> 1382      40G5 1998     51.53941  6.62076041    1998_40G5 niche_data 189
#> 1383      38G3 2004     33.62888  6.56766474    2004_38G3   all_data 193
#> 1384      40G6 2014     50.04572  5.20312834    2014_40G6   all_data 193
#> 1385      40G7 2006     38.00000  7.51147881    2006_40G7   all_data 193
#> 1386      40H0 2015     44.00000  7.33364610    2015_40H0   all_data 193
#> 1387      38G3 2004     33.62888  6.56766474    2004_38G3 niche_data 193
#> 1388      40G6 2014     47.69748  5.31903612    2014_40G6 niche_data 193
#> 1389      40G7 2006     38.00000  7.51147881    2006_40G7 niche_data 193
#> 1390      40H0 2015     44.00000  7.33364610    2015_40H0 niche_data 193
#> 1391      38G4 2015     25.69453  6.74307953    2015_38G4   all_data 194
#> 1392      39G4 1996     40.00000  4.47968259    1996_39G4   all_data 194
#> 1393      39G4 1999     40.00000  4.41341546    1999_39G4   all_data 194
#> 1394      40H0 2006     44.00000  7.27376887    2006_40H0   all_data 194
#> 1395      38G4 2015     25.69453  6.74307953    2015_38G4 niche_data 194
#> 1396      39G4 1996     40.00000  4.48685499    1996_39G4 niche_data 194
#> 1397      39G4 1999     40.00000  4.42516197    1999_39G4 niche_data 194
#> 1398      40H0 2006     44.00000  7.27376887    2006_40H0 niche_data 194
#> 1399      39G4 2011     40.00000  4.22363715    2011_39G4   all_data 195
#> 1400      40G5 2000     62.00000  4.54194870    2000_40G5   all_data 195
#> 1401      41G9 1997    123.90523  3.09832028    1997_41G9   all_data 195
#> 1402      39G4 2011     40.00000  4.22363715    2011_39G4 niche_data 195
#> 1403      40G5 2000     50.75644  6.80719254    2000_40G5 niche_data 195
#> 1404      41G9 1997     59.00000  7.06970237    1997_41G9 niche_data 195
#> 1405      40G6 2012     50.04572  6.36384994    2012_40G6   all_data 196
#> 1406      40G6 2012     50.00000  6.38618102    2012_40G6 niche_data 196
#> 1407      38G5 2010     61.20802  5.32932171    2010_38G5   all_data 200
#> 1408      38G9 2010     91.00641  4.56664470    2010_38G9   all_data 200
#> 1409      39G8 2010     90.00000  3.73516335    2010_39G8   all_data 200
#> 1410      40G5 2001     62.00000  4.11904586    2001_40G5   all_data 200
#> 1411      40H0 2003     44.00000  7.01380089    2003_40H0   all_data 200
#> 1412      38G5 2010     61.20802  5.32932171    2010_38G5 niche_data 200
#> 1413      38G9 2010     61.59609  6.32445764    2010_38G9 niche_data 200
#> 1414      39G8 2010     88.00000  3.96425555    2010_39G8 niche_data 200
#> 1415      40G5 2001     55.61383  5.61290493    2001_40G5 niche_data 200
#> 1416      40H0 2003     44.00000  7.01380089    2003_40H0 niche_data 200
#> 1417      38G4 2010     25.69453  7.20360099    2010_38G4   all_data 202
#> 1418      38G4 2010     25.69453  7.20360099    2010_38G4 niche_data 202
#> 1419      43G7 1995     83.80202  3.77081181    1995_43G7   all_data 203
#> 1420      43G7 1995     60.79264  6.58410182    1995_43G7 niche_data 203
#> 1421      39G4 1998     40.00000  4.66309353    1998_39G4   all_data 204
#> 1422      40G7 2005     38.00000  7.58975298    2005_40G7   all_data 204
#> 1423      39G4 1998     40.00000  4.67871797    1998_39G4 niche_data 204
#> 1424      40G7 2005     38.00000  7.58975298    2005_40G7 niche_data 204
#> 1425      41G8 1999     96.71104  1.07823250    1999_41G8   all_data 205
#> 1426      41G8 1999     36.96864  7.90023805    1999_41G8 niche_data 205
#> 1427      40G4 2009     39.57072  7.47342278    2009_40G4   all_data 206
#> 1428      40G4 2009     36.69833  7.58056192    2009_40G4 niche_data 206
#> 1429      38G4 2012     25.69453  7.18056526    2012_38G4   all_data 207
#> 1430      39G3 1993     34.94275  6.71160603    1993_39G3   all_data 207
#> 1431      39G3 2002     34.94275  6.60521452    2002_39G3   all_data 207
#> 1432      38G4 2012     25.69453  7.18056526    2012_38G4 niche_data 207
#> 1433      39G3 1993     34.94275  6.71160603    1993_39G3 niche_data 207
#> 1434      39G3 2002     34.94275  6.60521452    2002_39G3 niche_data 207
#> 1435      39G3 2000     34.94275  6.00887537    2000_39G3   all_data 208
#> 1436      39G6 2007     68.00000  3.31123486    2007_39G6   all_data 208
#> 1437      40G8 2004     97.98204  2.52725546    2004_40G8   all_data 208
#> 1438      39G3 2000     34.94275  6.00887537    2000_39G3 niche_data 208
#> 1439      39G6 2007     58.36765  4.18453640    2007_39G6 niche_data 208
#> 1440      40G8 2004     90.00000  2.95856304    2004_40G8 niche_data 208
#> 1441      38G3 2018     33.62888  6.45595207    2018_38G3   all_data 210
#> 1442      40G4 2003     39.57072  7.59007593    2003_40G4   all_data 210
#> 1443      40G5 1995     62.00000  4.52049900    1995_40G5   all_data 210
#> 1444      38G3 2018     33.62888  6.45595207    2018_38G3 niche_data 210
#> 1445      40G4 2003     37.71805  7.65977962    2003_40G4 niche_data 210
#> 1446      40G5 1995     50.75644  6.55325084    1995_40G5 niche_data 210
#> 1447      39G4 2017     40.00000  3.91699404    2017_39G4   all_data 211
#> 1448      39G9 2006     99.00000  3.01744072    2006_39G9   all_data 211
#> 1449      39G4 2017     40.00000  3.97061634    2017_39G4 niche_data 211
#> 1450      39G9 2006     88.00000  4.00799848    2006_39G9 niche_data 211
#> 1451      38G8 2007     40.19787  6.53047515    2007_38G8   all_data 213
#> 1452      40G5 2002     62.00000  4.47873125    2002_40G5   all_data 213
#> 1453      40H0 2012     44.00000  7.20318067    2012_40H0   all_data 213
#> 1454      38G8 2007     28.72128  6.80436577    2007_38G8 niche_data 213
#> 1455      40G5 2002     49.32156  7.50633729    2002_40G5 niche_data 213
#> 1456      40H0 2012     44.00000  7.20318067    2012_40H0 niche_data 213
#> 1457      38G5 2015     61.20802  4.03259975    2015_38G5   all_data 214
#> 1458      43H1 2017     23.00000  7.55194696    2017_43H1   all_data 214
#> 1459      38G5 2015     57.00000  4.54687731    2015_38G5 niche_data 214
#> 1460      43H1 2017     23.00000  7.55194696    2017_43H1 niche_data 214
#> 1461      40G5 1999     62.00000  2.88442435    1999_40G5   all_data 215
#> 1462      40G5 1999     50.59480  5.83334434    1999_40G5 niche_data 215
#> 1463      41G8 2006     96.71104  2.00905323    2006_41G8   all_data 216
#> 1464      42H0 2011     57.59084  7.38278525    2011_42H0   all_data 216
#> 1465      41G8 2006     39.00000  7.38339249    2006_41G8 niche_data 216
#> 1466      42H0 2011     42.46936  7.48748806    2011_42H0 niche_data 216
#> 1467      40G5 2009     62.00000  3.94905848    2009_40G5   all_data 217
#> 1468      40G5 2009     51.73441  6.95365722    2009_40G5 niche_data 217
#> 1469      39G7 2014     56.11417  6.08086651    2014_39G7   all_data 218
#> 1470      39G7 2014     52.79956  6.20612401    2014_39G7 niche_data 218
#> 1471      40G5 2010     62.00000  5.32314062    2010_40G5   all_data 220
#> 1472      40G5 2010     54.84511  6.52124616    2010_40G5 niche_data 220
#> 1473      40G6 2004     50.04572  6.38714723    2004_40G6   all_data 223
#> 1474      40G6 2004     49.98706  6.41719069    2004_40G6 niche_data 223
#> 1475      40G5 2003     62.00000  4.21091373    2003_40G5   all_data 224
#> 1476      42H0 2009     57.59084  6.83890310    2009_42H0   all_data 224
#> 1477      40G5 2003     59.89549  4.76390229    2003_40G5 niche_data 224
#> 1478      42H0 2009     43.82713  7.44053942    2009_42H0 niche_data 224
#> 1479      39G5 2016     85.00000 -0.39387797    2016_39G5   all_data 226
#> 1480      40G8 2014     97.98204  3.81500451    2014_40G8   all_data 226
#> 1481      39G5 2016     72.00000  3.52131551    2016_39G5 niche_data 226
#> 1482      40G8 2014     90.00000  4.16501806    2014_40G8 niche_data 226
#> 1483      38G9 2007     91.00641  4.09010561    2007_38G9   all_data 227
#> 1484      38G9 2007     61.59609  6.26648915    2007_38G9 niche_data 227
#> 1485      40G4 2014     39.57072  6.83985677    2014_40G4   all_data 228
#> 1486      40G4 2014     35.44424  6.97138760    2014_40G4 niche_data 228
#> 1487      39G4 2015     40.00000  4.33386796    2015_39G4   all_data 229
#> 1488      39G8 2004     90.00000  3.59944719    2004_39G8   all_data 229
#> 1489      39H0 2016     28.00000  7.69683472    2016_39H0   all_data 229
#> 1490      39G4 2015     40.00000  4.36143281    2015_39G4 niche_data 229
#> 1491      39G8 2004     88.00000  3.73366252    2004_39G8 niche_data 229
#> 1492      39H0 2016     28.00000  7.69683472    2016_39H0 niche_data 229
#> 1493      41G7 2016     38.00000  7.17534602    2016_41G7   all_data 230
#> 1494      41G7 2016     38.00000  7.17534602    2016_41G7 niche_data 230
#> 1495      38G3 2017     33.62888  6.26329053    2017_38G3   all_data 232
#> 1496      38G3 2017     33.62888  6.26329053    2017_38G3 niche_data 232
#> 1497      39G4 2006     40.00000  3.43167428    2006_39G4   all_data 233
#> 1498      39G4 2006     38.33901  3.92284436    2006_39G4 niche_data 233
#> 1499      38G9 2016     91.00641  4.74613916    2016_38G9   all_data 236
#> 1500      39G3 2018     34.94275  5.74982083    2018_39G3   all_data 236
#> 1501      38G9 2016     61.59609  6.49544926    2016_38G9 niche_data 236
#> 1502      39G3 2018     34.94275  5.74982083    2018_39G3 niche_data 236
#> 1503      39G9 2005     99.00000  2.95421500    2005_39G9   all_data 238
#> 1504      42H0 2005     57.59084  6.78418560    2005_42H0   all_data 238
#> 1505      39G9 2005     86.00000  4.58550102    2005_39G9 niche_data 238
#> 1506      42H0 2005     43.82713  7.53859552    2005_42H0 niche_data 238
#> 1507      38G5 2019     61.20802  5.04092681    2019_38G5   all_data 239
#> 1508      40H0 2011     44.00000  7.24101696    2011_40H0   all_data 239
#> 1509      38G5 2019     58.00000  5.27318508    2019_38G5 niche_data 239
#> 1510      40H0 2011     43.95792  7.24135371    2011_40H0 niche_data 239
#> 1511      40G6 2013     50.04572  6.71865841    2013_40G6   all_data 240
#> 1512      40G6 2013     50.04572  6.71865841    2013_40G6 niche_data 240
#> 1513      38G2 2002     17.00000  6.94486760    2002_38G2   all_data 241
#> 1514      38G4 2011     25.69453  7.13986849    2011_38G4   all_data 241
#> 1515      40G7 2000     38.00000  7.38030073    2000_40G7   all_data 241
#> 1516      38G2 2002     17.00000  6.94486760    2002_38G2 niche_data 241
#> 1517      38G4 2011     25.69453  7.13986849    2011_38G4 niche_data 241
#> 1518      40G7 2000     38.00000  7.38030073    2000_40G7 niche_data 241
#> 1519      39G3 2009     34.94275  5.77587992    2009_39G3   all_data 242
#> 1520      39G5 2008     85.00000  0.59044380    2008_39G5   all_data 242
#> 1521      39G3 2009     34.94275  5.77587992    2009_39G3 niche_data 242
#> 1522      39G5 2008     75.24509  2.96117508    2008_39G5 niche_data 242
#> 1523      38G4 2008     25.69453  6.62331356    2008_38G4   all_data 243
#> 1524      38G4 2008     25.69453  6.62331356    2008_38G4 niche_data 243
#> 1525      40G4 2010     39.57072  7.42194525    2010_40G4   all_data 244
#> 1526      40G4 2010     37.64887  7.50670972    2010_40G4 niche_data 244
#> 1527      40G5 2012     62.00000  4.73090462    2012_40G5   all_data 246
#> 1528      42H0 2004     57.59084  7.43776512    2004_42H0   all_data 246
#> 1529      40G5 2012     54.33871  5.99160081    2012_40G5 niche_data 246
#> 1530      42H0 2004     43.82713  7.51005227    2004_42H0 niche_data 246
#> 1531      41G8 2000     96.71104  1.64182787    2000_41G8   all_data 247
#> 1532      41G8 2000     38.46899  7.27885830    2000_41G8 niche_data 247
#> 1533      38G3 2009     33.62888  6.12160992    2009_38G3   all_data 249
#> 1534      38G4 2018     25.69453  6.95635402    2018_38G4   all_data 249
#> 1535      38G3 2009     33.62888  6.12160992    2009_38G3 niche_data 249
#> 1536      38G4 2018     25.69453  6.95635402    2018_38G4 niche_data 249
#> 1537      38G3 2014     33.62888  6.22554047    2014_38G3   all_data 250
#> 1538      40H0 2014     44.00000  7.39396548    2014_40H0   all_data 250
#> 1539      38G3 2014     33.62888  6.22554047    2014_38G3 niche_data 250
#> 1540      40H0 2014     44.00000  7.39396548    2014_40H0 niche_data 250
#> 1541      40G6 1994     50.04572  6.15281285    1994_40G6   all_data 251
#> 1542      40G6 1994     45.53600  6.61487227    1994_40G6 niche_data 251
#> 1543      39G3 2006     34.94275  4.57857950    2006_39G3   all_data 252
#> 1544      39G3 2006     32.56590  5.93766631    2006_39G3 niche_data 252
#> 1545      40G4 2018     39.57072  7.13892734    2018_40G4   all_data 256
#> 1546      40G4 2018     35.44424  7.36906676    2018_40G4 niche_data 256
#> 1547      40G5 2015     62.00000  4.26228733    2015_40G5   all_data 257
#> 1548      40G5 2015     53.09659  5.60726855    2015_40G5 niche_data 257
#> 1549      44H1 2009     30.23161  7.50363995    2009_44H1   all_data 258
#> 1550      44H1 2009     30.23161  7.50363995    2009_44H1 niche_data 258
#> 1551      39G8 2000     90.00000  3.79280105    2000_39G8   all_data 259
#> 1552      39G8 2000     88.00000  4.13117193    2000_39G8 niche_data 259
#> 1553      38G3 2003     33.62888  6.74240464    2003_38G3   all_data 260
#> 1554      38G4 2004     25.69453  7.10132609    2004_38G4   all_data 260
#> 1555      38G3 2003     33.62888  6.74240464    2003_38G3 niche_data 260
#> 1556      38G4 2004     25.69453  7.10132609    2004_38G4 niche_data 260
#> 1557      40G5 2017     62.00000  4.46488940    2017_40G5   all_data 264
#> 1558      43H0 2007    124.04904  1.23906355    2007_43H0   all_data 264
#> 1559      40G5 2017     53.15983  5.46546473    2017_40G5 niche_data 264
#> 1560      43H0 2007     52.76306  7.34892010    2007_43H0 niche_data 264
#> 1561      38G3 2013     33.62888  6.53528029    2013_38G3   all_data 266
#> 1562      38G3 2013     33.62888  6.53528029    2013_38G3 niche_data 266
#> 1563      41G8 2005     96.71104  1.84722230    2005_41G8   all_data 268
#> 1564      41G8 2005     39.02779  7.61090698    2005_41G8 niche_data 268
#> 1565      40G4 2002     39.57072  7.66659697    2002_40G4   all_data 270
#> 1566      40G4 2002     36.50172  7.70767242    2002_40G4 niche_data 270
#> 1567      38G5 2017     61.20802  3.97708334    2017_38G5   all_data 272
#> 1568      38G5 2017     58.28108  4.24452656    2017_38G5 niche_data 272
#> 1569      40G4 2006     39.57072  6.67227117    2006_40G4   all_data 276
#> 1570      40G5 2014     62.00000  2.62256883    2014_40G5   all_data 276
#> 1571      40G6 2017     50.04572  5.92699738    2017_40G6   all_data 276
#> 1572      40G4 2006     37.41398  6.75615888    2006_40G4 niche_data 276
#> 1573      40G5 2014     50.59480  5.69509136    2014_40G5 niche_data 276
#> 1574      40G6 2017     50.00000  5.94242792    2017_40G6 niche_data 276
#> 1575      40G7 2008     38.00000  7.53641807    2008_40G7   all_data 280
#> 1576      40G7 2008     38.00000  7.53641807    2008_40G7 niche_data 280
#> 1577      39G3 1998     34.94275  6.69857929    1998_39G3   all_data 281
#> 1578      39G3 1998     34.94275  6.69857929    1998_39G3 niche_data 281
#> 1579      40G6 2006     50.04572  6.31682059    2006_40G6   all_data 286
#> 1580      40G6 2006     48.62833  6.51699351    2006_40G6 niche_data 286
#> 1581      40G9 2007     83.07490  2.85300199    2007_40G9   all_data 287
#> 1582      40G9 2007     80.45032  3.25039552    2007_40G9 niche_data 287
#> 1583      39G3 1995     34.94275  5.16173920    1995_39G3   all_data 294
#> 1584      39G3 1995     34.94275  5.16173920    1995_39G3 niche_data 294
#> 1585      40G7 2007     38.00000  7.31781247    2007_40G7   all_data 297
#> 1586      40G7 2007     38.00000  7.31781247    2007_40G7 niche_data 297
#> 1587      40G4 2008     39.57072  6.56223490    2008_40G4   all_data 298
#> 1588      40G6 2005     50.04572  6.12347604    2005_40G6   all_data 298
#> 1589      40G4 2008     37.71805  6.68364566    2008_40G4 niche_data 298
#> 1590      40G6 2005     42.13489  7.47465361    2005_40G6 niche_data 298
#> 1591      38G3 2019     33.62888  6.47869123    2019_38G3   all_data 299
#> 1592      38G3 2019     33.62888  6.47869123    2019_38G3 niche_data 299
#> 1593      38G2 2004     17.00000  6.87194553    2004_38G2   all_data 300
#> 1594      40G4 2016     39.57072  6.78595284    2016_40G4   all_data 300
#> 1595      38G2 2004     17.00000  6.87194553    2004_38G2 niche_data 300
#> 1596      40G4 2016     36.69833  6.98383364    2016_40G4 niche_data 300
#> 1597      39G9 2016     99.00000  2.99028008    2016_39G9   all_data 301
#> 1598      39G9 2016     86.00000  4.48798564    2016_39G9 niche_data 301
#> 1599      38G3 2001     33.62888  6.42969077    2001_38G3   all_data 302
#> 1600      40H0 2010     44.00000  7.48592706    2010_40H0   all_data 302
#> 1601      38G3 2001     33.62888  6.42969077    2001_38G3 niche_data 302
#> 1602      40H0 2010     44.00000  7.48592706    2010_40H0 niche_data 302
#> 1603      39G3 2007     34.94275  5.77635299    2007_39G3   all_data 304
#> 1604      39G3 2007     34.94275  5.77635299    2007_39G3 niche_data 304
#> 1605      40G5 2005     62.00000  3.34164608    2005_40G5   all_data 305
#> 1606      40G5 2005     51.77586  6.49334874    2005_40G5 niche_data 305
#> 1607      39G8 2014     90.00000  4.53384141    2014_39G8   all_data 307
#> 1608      39G8 2014     88.00000  4.71667328    2014_39G8 niche_data 307
#> 1609      39G4 2008     40.00000  3.98615354    2008_39G4   all_data 309
#> 1610      39G4 2008     40.00000  3.98615354    2008_39G4 niche_data 309
#> 1611      39G7 2006     56.11417  5.76865155    2006_39G7   all_data 310
#> 1612      39G7 2006     52.79956  6.11419265    2006_39G7 niche_data 310
#> 1613      38G3 2016     33.62888  6.58433350    2016_38G3   all_data 311
#> 1614      38G3 2016     33.62888  6.58433350    2016_38G3 niche_data 311
#> 1615      38G3 2010     33.62888  6.75243397    2010_38G3   all_data 313
#> 1616      38G3 2010     33.62888  6.75243397    2010_38G3 niche_data 313
#> 1617      40H0 2013     44.00000  7.57399354    2013_40H0   all_data 315
#> 1618      40H0 2013     44.00000  7.57399354    2013_40H0 niche_data 315
#> 1619      40G6 2016     50.04572  5.82044752    2016_40G6   all_data 316
#> 1620      40G6 2016     49.14320  6.07540381    2016_40G6 niche_data 316
#> 1621      38G3 2012     33.62888  6.16972177    2012_38G3   all_data 318
#> 1622      38G3 2012     33.62888  6.16972177    2012_38G3 niche_data 318
#> 1623      38G3 1995     33.62888  6.50902636    1995_38G3   all_data 323
#> 1624      38G3 1995     33.62888  6.50902636    1995_38G3 niche_data 323
#> 1625      38G3 1993     33.62888  6.05533116    1993_38G3   all_data 324
#> 1626      38G3 1993     33.62888  6.05533116    1993_38G3 niche_data 324
#> 1627      40G4 2005     39.57072  7.12724207    2005_40G4   all_data 325
#> 1628      42H0 2001     57.59084  7.36111084    2001_42H0   all_data 325
#> 1629      40G4 2005     36.50172  7.23012774    2005_40G4 niche_data 325
#> 1630      42H0 2001     43.82713  7.62898127    2001_42H0 niche_data 325
#> 1631      38G9 2006     91.00641  4.77014989    2006_38G9   all_data 326
#> 1632      38G9 2006     61.59609  6.57779169    2006_38G9 niche_data 326
#> 1633      39G3 2005     34.94275  5.53513159    2005_39G3   all_data 329
#> 1634      39G3 2005     34.94275  5.53513159    2005_39G3 niche_data 329
#> 1635      38G4 2017     25.69453  7.03468798    2017_38G4   all_data 334
#> 1636      38G4 2017     25.69453  7.03468798    2017_38G4 niche_data 334
#> 1637      38G5 2018     61.20802  4.47664504    2018_38G5   all_data 337
#> 1638      38G5 2018     60.50000  4.52082800    2018_38G5 niche_data 337
#> 1639      40H0 2018     44.00000  6.49050152    2018_40H0   all_data 344
#> 1640      40H0 2018     44.00000  6.49050152    2018_40H0 niche_data 344
#> 1641      40G6 2007     50.04572  6.14412260    2007_40G6   all_data 346
#> 1642      40G6 2007     42.81335  6.80270918    2007_40G6 niche_data 346
#> 1643      40G8 2015     97.98204  3.22956684    2015_40G8   all_data 347
#> 1644      40G8 2015     90.00000  3.57221799    2015_40G8 niche_data 347
#> 1645      39G3 2008     34.94275  4.84344067    2008_39G3   all_data 350
#> 1646      39G3 2008     34.94275  4.84344067    2008_39G3 niche_data 350
#> 1647      39G9 2017     99.00000  2.73148844    2017_39G9   all_data 354
#> 1648      39G9 2017     86.93832  4.23007106    2017_39G9 niche_data 354
#> 1649      38G3 2015     33.62888  6.32978149    2015_38G3   all_data 356
#> 1650      38G3 2015     33.62888  6.32978149    2015_38G3 niche_data 356
#> 1651      38G3 1997     33.62888  6.26388525    1997_38G3   all_data 360
#> 1652      38G3 1997     32.00000  6.32966391    1997_38G3 niche_data 360
#> 1653      39G4 2016     40.00000  4.68997244    2016_39G4   all_data 365
#> 1654      39G4 2016     40.00000  4.79822460    2016_39G4 niche_data 365
#> 1655      39H0 2003     28.00000  7.33174122    2003_39H0   all_data 366
#> 1656      40H0 2016     44.00000  7.47126105    2016_40H0   all_data 366
#> 1657      39H0 2003     28.00000  7.33174122    2003_39H0 niche_data 366
#> 1658      40H0 2016     44.00000  7.47126105    2016_40H0 niche_data 366
#> 1659      40G5 2007     62.00000  3.55162529    2007_40G5   all_data 367
#> 1660      40G5 2007     49.91640  6.19622590    2007_40G5 niche_data 367
#> 1661      39H0 2009     28.00000  7.54918193    2009_39H0   all_data 378
#> 1662      39H0 2009     28.00000  7.54918193    2009_39H0 niche_data 378
#> 1663      39H0 2017     28.00000  7.48334568    2017_39H0   all_data 382
#> 1664      39H0 2017     28.00000  7.48334568    2017_39H0 niche_data 382
#> 1665      39G4 2005     40.00000  3.55510765    2005_39G4   all_data 392
#> 1666      39G4 2005     38.77253  3.89016674    2005_39G4 niche_data 392
#> 1667      38G3 2011     33.62888  6.30857395    2011_38G3   all_data 394
#> 1668      38G4 2014     25.69453  6.66188792    2014_38G4   all_data 394
#> 1669      38G9 2005     91.00641  4.86454627    2005_38G9   all_data 394
#> 1670      38G3 2011     33.62888  6.30857395    2011_38G3 niche_data 394
#> 1671      38G4 2014     25.69453  6.66188792    2014_38G4 niche_data 394
#> 1672      38G9 2005     61.59609  6.41117810    2005_38G9 niche_data 394
#> 1673      38G3 1998     33.62888  6.94903748    1998_38G3   all_data 401
#> 1674      38G3 1998     33.62888  6.94903748    1998_38G3 niche_data 401
#> 1675      39G8 2015     90.00000  4.22337099    2015_39G8   all_data 404
#> 1676      39G8 2015     88.00000  4.52490510    2015_39G8 niche_data 404
#> 1677      39G9 2007     99.00000  1.77037469    2007_39G9   all_data 409
#> 1678      39G9 2007     86.00000  3.89777069    2007_39G9 niche_data 409
#> 1679      39G4 2007     40.00000  4.09226260    2007_39G4   all_data 413
#> 1680      39G4 2007     40.00000  4.20912820    2007_39G4 niche_data 413
#> 1681      39G6 2004     68.00000  3.75405150    2004_39G6   all_data 414
#> 1682      39G6 2004     62.09842  4.31340779    2004_39G6 niche_data 414
#> 1683      39G7 2004     56.11417  6.06482046    2004_39G7   all_data 416
#> 1684      39G7 2004     52.79956  6.29170326    2004_39G7 niche_data 416
#> 1685      39G9 2008     99.00000  2.12612212    2008_39G9   all_data 422
#> 1686      39G9 2008     85.00000  3.57528645    2008_39G9 niche_data 422
#> 1687      40H0 2017     44.00000  7.38990653    2017_40H0   all_data 426
#> 1688      40H0 2017     44.00000  7.38990653    2017_40H0 niche_data 426
#> 1689      40H0 2004     44.00000  7.51056415    2004_40H0   all_data 427
#> 1690      40H0 2004     44.00000  7.51056415    2004_40H0 niche_data 427
#> 1691      39G9 2003     99.00000  3.55108107    2003_39G9   all_data 429
#> 1692      39G9 2003     88.00000  4.83712501    2003_39G9 niche_data 429
#> 1693      38G3 1999     33.62888  6.36611363    1999_38G3   all_data 431
#> 1694      38G3 1999     33.62888  6.36611363    1999_38G3 niche_data 431
#> 1695      39H0 2007     28.00000  7.39940943    2007_39H0   all_data 435
#> 1696      39H0 2007     28.00000  7.39940943    2007_39H0 niche_data 435
#> 1697      39G7 2007     56.11417  5.72339244    2007_39G7   all_data 456
#> 1698      39G7 2007     52.79956  5.98298178    2007_39G7 niche_data 456
#> 1699      38G5 2016     61.20802  4.52147285    2016_38G5   all_data 463
#> 1700      38G5 2016     58.28108  4.90933704    2016_38G5 niche_data 463
#> 1701      40H0 2007     44.00000  7.25713292    2007_40H0   all_data 475
#> 1702      40H0 2007     44.00000  7.25713292    2007_40H0 niche_data 475
#> 1703      39H0 2010     28.00000  7.71854084    2010_39H0   all_data 478
#> 1704      39H0 2010     28.00000  7.71854084    2010_39H0 niche_data 478
#> 1705      39H0 2011     28.00000  7.41536649    2011_39H0   all_data 487
#> 1706      39H0 2011     28.00000  7.41536649    2011_39H0 niche_data 487
#> 1707      38G3 1994     33.62888  6.34268935    1994_38G3   all_data 525
#> 1708      38G3 1994     33.62888  6.34268935    1994_38G3 niche_data 525
#> 1709      38G3 1996     33.62888  6.44059147    1996_38G3   all_data 536
#> 1710      38G3 1996     33.62888  6.44059147    1996_38G3 niche_data 536
#> 1711      38G3 2000     33.62888  5.90190646    2000_38G3   all_data 542
#> 1712      38G3 2000     33.62888  5.90190646    2000_38G3 niche_data 542
#> 1713      39H0 2005     28.00000  7.44705004    2005_39H0   all_data 547
#> 1714      39H0 2005     28.00000  7.44705004    2005_39H0 niche_data 547
#> 1715      40G4 2007     39.57072  6.95121599    2007_40G4   all_data 548
#> 1716      40G4 2007     36.54355  7.18021568    2007_40G4 niche_data 548
#> 1717      39G9 2010     99.00000  2.54671022    2010_39G9   all_data 604
#> 1718      39G9 2010     86.00000  3.55012318    2010_39G9 niche_data 604
#> 1719      40H0 2008     44.00000  7.44600027    2008_40H0   all_data 612
#> 1720      40H0 2008     44.00000  7.44600027    2008_40H0 niche_data 612
#> 1721      39H0 2008     28.00000  7.58129818    2008_39H0   all_data 629
#> 1722      39H0 2008     28.00000  7.58129818    2008_39H0 niche_data 629
#> 1723      40G5 2016     62.00000  3.65039028    2016_40G5   all_data 639
#> 1724      40G5 2016     52.40050  5.59578437    2016_40G5 niche_data 639
#> 1725      38G4 2016     25.69453  7.03223266    2016_38G4   all_data 763
#> 1726      38G4 2016     25.69453  7.03223266    2016_38G4 niche_data 763
#> 1727      40H0 2005     44.00000  7.46364980    2005_40H0   all_data 778
#> 1728      40H0 2005     44.00000  7.46364980    2005_40H0 niche_data 778
#> 1729      38G5 2004     61.20802  3.72170067    2004_38G5   all_data 798
#> 1730      38G5 2004     59.44766  3.91981613    2004_38G5 niche_data 798
#> 1731      39H0 2006     28.00000  7.47667114    2006_39H0   all_data 836
#> 1732      39H0 2006     28.00000  7.47667114    2006_39H0 niche_data 836
#> 1733      37G2 1993     14.28574  4.20511419    1993_37G2   all_data  NA
#> 1734      37G2 1994     14.28574  6.63390811    1994_37G2   all_data  NA
#> 1735      37G2 1995     14.28574  5.96333062    1995_37G2   all_data  NA
#> 1736      37G2 1996     14.28574  6.17467651    1996_37G2   all_data  NA
#> 1737      37G2 1997     14.28574  6.30160302    1997_37G2   all_data  NA
#> 1738      37G2 1998     14.28574  6.68297993    1998_37G2   all_data  NA
#> 1739      37G2 1999     14.28574  5.75838467    1999_37G2   all_data  NA
#> 1740      37G2 2000     14.28574  5.27109639    2000_37G2   all_data  NA
#> 1741      37G2 2001     14.28574  6.09256122    2001_37G2   all_data  NA
#> 1742      37G2 2004     14.28574  6.04276928    2004_37G2   all_data  NA
#> 1743      37G2 2007     14.28574  6.29931225    2007_37G2   all_data  NA
#> 1744      37G2 2018     14.28574  6.57808544    2018_37G2   all_data  NA
#> 1745      37G3 1993     15.00000  7.76069318    1993_37G3   all_data  NA
#> 1746      37G3 1995     15.00000  7.17451859    1995_37G3   all_data  NA
#> 1747      37G3 1996     15.00000  7.15662002    1996_37G3   all_data  NA
#> 1748      37G3 1997     15.00000  7.23679535    1997_37G3   all_data  NA
#> 1749      37G3 1998     15.00000  7.85840117    1998_37G3   all_data  NA
#> 1750      37G3 1999     15.00000  6.86896506    1999_37G3   all_data  NA
#> 1751      37G3 2000     15.00000  7.12327788    2000_37G3   all_data  NA
#> 1752      37G3 2001     15.00000  7.20570204    2001_37G3   all_data  NA
#> 1753      37G3 2002     15.00000  7.56770930    2002_37G3   all_data  NA
#> 1754      37G3 2003     15.00000  7.22337769    2003_37G3   all_data  NA
#> 1755      37G3 2007     15.00000  7.28948577    2007_37G3   all_data  NA
#> 1756      37G3 2017     15.00000  7.67859773    2017_37G3   all_data  NA
#> 1757      37G4 1994     13.00000  7.58559337    1994_37G4   all_data  NA
#> 1758      37G4 1995     13.00000  7.39707206    1995_37G4   all_data  NA
#> 1759      37G4 1996     13.00000  7.53205651    1996_37G4   all_data  NA
#> 1760      37G4 1998     13.00000  7.83061633    1998_37G4   all_data  NA
#> 1761      37G4 1999     13.00000  7.44677978    1999_37G4   all_data  NA
#> 1762      37G4 2000     13.00000  7.48420390    2000_37G4   all_data  NA
#> 1763      37G4 2001     13.00000  7.45198553    2001_37G4   all_data  NA
#> 1764      37G4 2003     13.00000  7.32028555    2003_37G4   all_data  NA
#> 1765      37G4 2010     13.00000  7.83301839    2010_37G4   all_data  NA
#> 1766      37G5 1993     20.00000  7.60992421    1993_37G5   all_data  NA
#> 1767      37G5 1994     20.00000  7.48136077    1994_37G5   all_data  NA
#> 1768      37G5 1995     20.00000  7.34416200    1995_37G5   all_data  NA
#> 1769      37G5 1996     20.00000  7.61639668    1996_37G5   all_data  NA
#> 1770      37G5 1997     20.00000  7.29925896    1997_37G5   all_data  NA
#> 1771      37G5 1998     20.00000  7.65867811    1998_37G5   all_data  NA
#> 1772      37G5 1999     20.00000  7.53702702    1999_37G5   all_data  NA
#> 1773      37G5 2000     20.00000  7.47179504    2000_37G5   all_data  NA
#> 1774      37G5 2001     20.00000  7.45119157    2001_37G5   all_data  NA
#> 1775      37G5 2006     20.00000  7.30919856    2006_37G5   all_data  NA
#> 1776      37G6 1993     18.00000  7.49108204    1993_37G6   all_data  NA
#> 1777      37G6 1994     18.00000  7.39499081    1994_37G6   all_data  NA
#> 1778      37G6 1995     18.00000  7.27448384    1995_37G6   all_data  NA
#> 1779      37G6 1996     18.00000  7.74132245    1996_37G6   all_data  NA
#> 1780      37G6 1997     18.00000  7.22383697    1997_37G6   all_data  NA
#> 1781      37G6 1998     18.00000  7.71196467    1998_37G6   all_data  NA
#> 1782      37G6 1999     18.00000  7.64088136    1999_37G6   all_data  NA
#> 1783      37G6 2000     18.00000  7.39410181    2000_37G6   all_data  NA
#> 1784      37G6 2001     18.00000  7.62648277    2001_37G6   all_data  NA
#> 1785      37G6 2002     18.00000  7.54646011    2002_37G6   all_data  NA
#> 1786      37G6 2004     18.00000  7.68284044    2004_37G6   all_data  NA
#> 1787      37G6 2005     18.00000  7.47241853    2005_37G6   all_data  NA
#> 1788      37G6 2006     18.00000  7.46757608    2006_37G6   all_data  NA
#> 1789      37G6 2007     18.00000  7.28740493    2007_37G6   all_data  NA
#> 1790      37G6 2015     18.00000  7.55594598    2015_37G6   all_data  NA
#> 1791      37G6 2016     18.00000  7.75680214    2016_37G6   all_data  NA
#> 1792      37G8 1993     20.86536  7.01664324    1993_37G8   all_data  NA
#> 1793      37G8 1994     20.86536  6.15052770    1994_37G8   all_data  NA
#> 1794      37G8 1995     20.86536  6.34913967    1995_37G8   all_data  NA
#> 1795      37G8 1996     20.86536  6.53975753    1996_37G8   all_data  NA
#> 1796      37G8 1997     20.86536  6.51565765    1997_37G8   all_data  NA
#> 1797      37G8 1998     20.86536  6.23302818    1998_37G8   all_data  NA
#> 1798      37G8 1999     20.86536  6.44170361    1999_37G8   all_data  NA
#> 1799      37G8 2000     20.86536  6.26792875    2000_37G8   all_data  NA
#> 1800      37G8 2001     20.86536  6.82311689    2001_37G8   all_data  NA
#> 1801      37G8 2002     20.86536  6.88362871    2002_37G8   all_data  NA
#> 1802      37G8 2003     20.86536  6.64862647    2003_37G8   all_data  NA
#> 1803      37G8 2004     20.86536  7.13540801    2004_37G8   all_data  NA
#> 1804      37G8 2005     20.86536  6.98302323    2005_37G8   all_data  NA
#> 1805      37G8 2006     20.86536  6.88736045    2006_37G8   all_data  NA
#> 1806      37G8 2008     20.86536  6.92182290    2008_37G8   all_data  NA
#> 1807      37G8 2009     20.86536  6.63211300    2009_37G8   all_data  NA
#> 1808      37G8 2010     20.86536  7.28464351    2010_37G8   all_data  NA
#> 1809      37G8 2011     20.86536  6.58978230    2011_37G8   all_data  NA
#> 1810      37G8 2012     20.86536  6.71990256    2012_37G8   all_data  NA
#> 1811      37G8 2013     20.86536  6.93181939    2013_37G8   all_data  NA
#> 1812      37G8 2016     20.86536  7.20219968    2016_37G8   all_data  NA
#> 1813      37G8 2017     20.86536  7.08395984    2017_37G8   all_data  NA
#> 1814      37G8 2018     20.86536  6.72066409    2018_37G8   all_data  NA
#> 1815      37G8 2019     20.86536  7.00984767    2019_37G8   all_data  NA
#> 1816      37G9 1993     43.07566  7.06676271    1993_37G9   all_data  NA
#> 1817      37G9 1994     43.07566  6.19283092    1994_37G9   all_data  NA
#> 1818      37G9 1995     43.07566  6.52132416    1995_37G9   all_data  NA
#> 1819      37G9 1996     43.07566  6.79768933    1996_37G9   all_data  NA
#> 1820      37G9 1997     43.07566  6.16910845    1997_37G9   all_data  NA
#> 1821      37G9 1998     43.07566  6.08435426    1998_37G9   all_data  NA
#> 1822      37G9 1999     43.07566  6.38760333    1999_37G9   all_data  NA
#> 1823      37G9 2000     43.07566  6.36699536    2000_37G9   all_data  NA
#> 1824      37G9 2001     43.07566  6.68125086    2001_37G9   all_data  NA
#> 1825      37G9 2002     43.07566  6.91182196    2002_37G9   all_data  NA
#> 1826      37G9 2014     43.07566  6.70335729    2014_37G9   all_data  NA
#> 1827      38G2 1993     17.00000  6.05552989    1993_38G2   all_data  NA
#> 1828      38G2 1994     17.00000  6.82655030    1994_38G2   all_data  NA
#> 1829      38G2 1995     17.00000  6.78852044    1995_38G2   all_data  NA
#> 1830      38G2 1996     17.00000  6.85859963    1996_38G2   all_data  NA
#> 1831      38G2 1997     17.00000  7.05134306    1997_38G2   all_data  NA
#> 1832      38G2 1998     17.00000  7.32619512    1998_38G2   all_data  NA
#> 1833      38G5 1993     61.20802  3.93238616    1993_38G5   all_data  NA
#> 1834      38G5 1994     61.20802  3.20767527    1994_38G5   all_data  NA
#> 1835      38G5 1995     61.20802  3.77393024    1995_38G5   all_data  NA
#> 1836      38G5 1996     61.20802  4.82126540    1996_38G5   all_data  NA
#> 1837      38G5 1997     61.20802  3.20753776    1997_38G5   all_data  NA
#> 1838      38G5 1998     61.20802  3.89813304    1998_38G5   all_data  NA
#> 1839      38G5 1999     61.20802  3.46964785    1999_38G5   all_data  NA
#> 1840      38G5 2001     61.20802  4.00514716    2001_38G5   all_data  NA
#> 1841      38G5 2002     61.20802  4.60370369    2002_38G5   all_data  NA
#> 1842      38G6 1993     26.52553  7.44933723    1993_38G6   all_data  NA
#> 1843      38G6 1994     26.52553  7.39133176    1994_38G6   all_data  NA
#> 1844      38G6 1995     26.52553  7.21339413    1995_38G6   all_data  NA
#> 1845      38G6 1996     26.52553  7.64821966    1996_38G6   all_data  NA
#> 1846      38G6 1997     26.52553  7.23463595    1997_38G6   all_data  NA
#> 1847      38G6 1998     26.52553  7.59544626    1998_38G6   all_data  NA
#> 1848      38G6 1999     26.52553  7.59171758    1999_38G6   all_data  NA
#> 1849      38G6 2001     26.52553  7.53696000    2001_38G6   all_data  NA
#> 1850      38G7 1993     21.00000  7.66297490    1993_38G7   all_data  NA
#> 1851      38G7 1994     21.00000  7.62870196    1994_38G7   all_data  NA
#> 1852      38G7 1995     21.00000  7.34043636    1995_38G7   all_data  NA
#> 1853      38G7 1996     21.00000  7.72085964    1996_38G7   all_data  NA
#> 1854      38G7 1997     21.00000  7.50529819    1997_38G7   all_data  NA
#> 1855      38G7 1998     21.00000  7.75350536    1998_38G7   all_data  NA
#> 1856      38G7 1999     21.00000  7.75870685    1999_38G7   all_data  NA
#> 1857      38G7 2000     21.00000  7.48371628    2000_38G7   all_data  NA
#> 1858      38G7 2001     21.00000  7.69145452    2001_38G7   all_data  NA
#> 1859      38G7 2003     21.00000  7.39710867    2003_38G7   all_data  NA
#> 1860      38G7 2006     21.00000  7.51662002    2006_38G7   all_data  NA
#> 1861      38G8 1993     40.19787  6.73713071    1993_38G8   all_data  NA
#> 1862      38G8 1994     40.19787  6.43502842    1994_38G8   all_data  NA
#> 1863      38G8 1995     40.19787  6.32884280    1995_38G8   all_data  NA
#> 1864      38G8 1996     40.19787  6.56708547    1996_38G8   all_data  NA
#> 1865      38G8 1997     40.19787  6.27642834    1997_38G8   all_data  NA
#> 1866      38G8 1998     40.19787  5.96843285    1998_38G8   all_data  NA
#> 1867      38G8 1999     40.19787  6.41948188    1999_38G8   all_data  NA
#> 1868      38G8 2000     40.19787  5.94023097    2000_38G8   all_data  NA
#> 1869      38G8 2001     40.19787  6.88402504    2001_38G8   all_data  NA
#> 1870      38G8 2002     40.19787  6.77307490    2002_38G8   all_data  NA
#> 1871      38G9 1993     91.00641  4.25710348    1993_38G9   all_data  NA
#> 1872      38G9 1994     91.00641  4.79218865    1994_38G9   all_data  NA
#> 1873      38G9 1995     91.00641  5.10249055    1995_38G9   all_data  NA
#> 1874      38G9 1996     91.00641  6.05044237    1996_38G9   all_data  NA
#> 1875      38G9 1997     91.00641  2.37103035    1997_38G9   all_data  NA
#> 1876      38G9 1998     91.00641  2.57706680    1998_38G9   all_data  NA
#> 1877      38G9 1999     91.00641  2.46085770    1999_38G9   all_data  NA
#> 1878      38G9 2000     91.00641  1.76079027    2000_38G9   all_data  NA
#> 1879      38G9 2001     91.00641  3.90812899    2001_38G9   all_data  NA
#> 1880      38G9 2002     91.00641  5.16704319    2002_38G9   all_data  NA
#> 1881      38G9 2009     91.00641  3.75242316    2009_38G9   all_data  NA
#> 1882      38H0 1993     25.40843  8.11768631    1993_38H0   all_data  NA
#> 1883      38H0 1994     25.40843  7.73542899    1994_38H0   all_data  NA
#> 1884      38H0 1995     25.40843  7.57679975    1995_38H0   all_data  NA
#> 1885      38H0 1996     25.40843  7.50906781    1996_38H0   all_data  NA
#> 1886      38H0 1997     25.40843  7.30467319    1997_38H0   all_data  NA
#> 1887      38H0 1998     25.40843  7.90287733    1998_38H0   all_data  NA
#> 1888      38H0 1999     25.40843  7.73133571    1999_38H0   all_data  NA
#> 1889      38H0 2000     25.40843  7.48040935    2000_38H0   all_data  NA
#> 1890      38H0 2001     25.40843  7.75845265    2001_38H0   all_data  NA
#> 1891      38H0 2002     25.40843  7.77892181    2002_38H0   all_data  NA
#> 1892      38H0 2003     25.40843  7.81739805    2003_38H0   all_data  NA
#> 1893      38H0 2004     25.40843  7.68964871    2004_38H0   all_data  NA
#> 1894      38H0 2005     25.40843  7.44535883    2005_38H0   all_data  NA
#> 1895      38H0 2006     25.40843  6.99158635    2006_38H0   all_data  NA
#> 1896      38H0 2007     25.40843  7.60890696    2007_38H0   all_data  NA
#> 1897      38H0 2008     25.40843  7.63600128    2008_38H0   all_data  NA
#> 1898      38H0 2009     25.40843  7.76965866    2009_38H0   all_data  NA
#> 1899      38H0 2010     25.40843  8.06326072    2010_38H0   all_data  NA
#> 1900      38H0 2011     25.40843  7.75620335    2011_38H0   all_data  NA
#> 1901      38H0 2012     25.40843  7.90984354    2012_38H0   all_data  NA
#> 1902      38H0 2013     25.40843  7.65943229    2013_38H0   all_data  NA
#> 1903      38H0 2014     25.40843  7.50772141    2014_38H0   all_data  NA
#> 1904      38H0 2015     25.40843  7.86688978    2015_38H0   all_data  NA
#> 1905      38H0 2016     25.40843  8.25953099    2016_38H0   all_data  NA
#> 1906      38H0 2017     25.40843  8.07327909    2017_38H0   all_data  NA
#> 1907      38H0 2018     25.40843  7.98754044    2018_38H0   all_data  NA
#> 1908      38H0 2019     25.40843  7.97582971    2019_38H0   all_data  NA
#> 1909      38H1 1993     19.05070  8.07086738    1993_38H1   all_data  NA
#> 1910      38H1 1994     19.05070  7.39894801    1994_38H1   all_data  NA
#> 1911      38H1 1995     19.05070  7.16752888    1995_38H1   all_data  NA
#> 1912      38H1 1996     19.05070  6.93238265    1996_38H1   all_data  NA
#> 1913      38H1 1997     19.05070  7.28553821    1997_38H1   all_data  NA
#> 1914      38H1 1998     19.05070  7.80666691    1998_38H1   all_data  NA
#> 1915      38H1 1999     19.05070  7.29755746    1999_38H1   all_data  NA
#> 1916      38H1 2000     19.05070  7.11444096    2000_38H1   all_data  NA
#> 1917      38H1 2001     19.05070  7.46701962    2001_38H1   all_data  NA
#> 1918      38H1 2002     19.05070  7.74018533    2002_38H1   all_data  NA
#> 1919      38H1 2003     19.05070  7.72443087    2003_38H1   all_data  NA
#> 1920      38H1 2004     19.05070  7.25328379    2004_38H1   all_data  NA
#> 1921      38H1 2005     19.05070  6.97672099    2005_38H1   all_data  NA
#> 1922      38H1 2006     19.05070  6.90305419    2006_38H1   all_data  NA
#> 1923      38H1 2007     19.05070  7.49408782    2007_38H1   all_data  NA
#> 1924      38H1 2008     19.05070  7.48430650    2008_38H1   all_data  NA
#> 1925      38H1 2009     19.05070  7.72732625    2009_38H1   all_data  NA
#> 1926      38H1 2010     19.05070  7.92293670    2010_38H1   all_data  NA
#> 1927      38H1 2011     19.05070  7.74005428    2011_38H1   all_data  NA
#> 1928      38H1 2012     19.05070  7.87143226    2012_38H1   all_data  NA
#> 1929      38H1 2013     19.05070  7.78247626    2013_38H1   all_data  NA
#> 1930      38H1 2014     19.05070  7.50278451    2014_38H1   all_data  NA
#> 1931      38H1 2015     19.05070  7.84719548    2015_38H1   all_data  NA
#> 1932      38H1 2016     19.05070  8.26214908    2016_38H1   all_data  NA
#> 1933      38H1 2017     19.05070  8.06476599    2017_38H1   all_data  NA
#> 1934      38H1 2018     19.05070  7.96728259    2018_38H1   all_data  NA
#> 1935      38H1 2019     19.05070  8.01308126    2019_38H1   all_data  NA
#> 1936      39G2 1993     19.00000  7.29217886    1993_39G2   all_data  NA
#> 1937      39G2 1994     19.00000  7.08028842    1994_39G2   all_data  NA
#> 1938      39G2 1995     19.00000  7.12534761    1995_39G2   all_data  NA
#> 1939      39G2 1996     19.00000  7.23473904    1996_39G2   all_data  NA
#> 1940      39G2 1997     19.00000  7.52556012    1997_39G2   all_data  NA
#> 1941      39G2 1998     19.00000  7.44898598    1998_39G2   all_data  NA
#> 1942      39G2 1999     19.00000  7.23632361    1999_39G2   all_data  NA
#> 1943      39G2 2001     19.00000  7.73925850    2001_39G2   all_data  NA
#> 1944      39G2 2003     19.00000  7.30094799    2003_39G2   all_data  NA
#> 1945      39G2 2004     19.00000  7.31416437    2004_39G2   all_data  NA
#> 1946      39G2 2005     19.00000  6.86376368    2005_39G2   all_data  NA
#> 1947      39G2 2009     19.00000  7.25172088    2009_39G2   all_data  NA
#> 1948      39G2 2010     19.00000  7.57748679    2010_39G2   all_data  NA
#> 1949      39G2 2012     19.00000  7.53586179    2012_39G2   all_data  NA
#> 1950      39G5 1994     85.00000  1.32231893    1994_39G5   all_data  NA
#> 1951      39G5 1995     85.00000  0.02901467    1995_39G5   all_data  NA
#> 1952      39G5 1997     85.00000  0.65457873    1997_39G5   all_data  NA
#> 1953      39G5 1998     85.00000  0.21451859    1998_39G5   all_data  NA
#> 1954      39G5 2000     85.00000 -0.77975655    2000_39G5   all_data  NA
#> 1955      39G5 2001     85.00000  1.06989232    2001_39G5   all_data  NA
#> 1956      39G5 2014     85.00000  0.76333313    2014_39G5   all_data  NA
#> 1957      39G6 1993     68.00000  4.49597131    1993_39G6   all_data  NA
#> 1958      39G6 1994     68.00000  2.93506846    1994_39G6   all_data  NA
#> 1959      39G6 1995     68.00000  3.04636055    1995_39G6   all_data  NA
#> 1960      39G6 1996     68.00000  4.49052279    1996_39G6   all_data  NA
#> 1961      39G6 1998     68.00000  3.66795816    1998_39G6   all_data  NA
#> 1962      39G6 1999     68.00000  3.28455310    1999_39G6   all_data  NA
#> 1963      39G6 2001     68.00000  3.29019701    2001_39G6   all_data  NA
#> 1964      39G6 2002     68.00000  3.29929326    2002_39G6   all_data  NA
#> 1965      39G7 1993     56.11417  6.48620451    1993_39G7   all_data  NA
#> 1966      39G7 1994     56.11417  6.13502179    1994_39G7   all_data  NA
#> 1967      39G7 1995     56.11417  6.03689879    1995_39G7   all_data  NA
#> 1968      39G7 1996     56.11417  6.69399534    1996_39G7   all_data  NA
#> 1969      39G7 1997     56.11417  6.09415120    1997_39G7   all_data  NA
#> 1970      39G7 1998     56.11417  5.97197878    1998_39G7   all_data  NA
#> 1971      39G7 1999     56.11417  6.01249510    1999_39G7   all_data  NA
#> 1972      39G7 2001     56.11417  6.31732678    2001_39G7   all_data  NA
#> 1973      39G8 1993     90.00000  4.97020557    1993_39G8   all_data  NA
#> 1974      39G8 1994     90.00000  3.74756687    1994_39G8   all_data  NA
#> 1975      39G8 1995     90.00000  4.10354855    1995_39G8   all_data  NA
#> 1976      39G8 1996     90.00000  5.10373877    1996_39G8   all_data  NA
#> 1977      39G8 1997     90.00000  4.16211860    1997_39G8   all_data  NA
#> 1978      39G8 1998     90.00000  3.40637995    1998_39G8   all_data  NA
#> 1979      39G8 1999     90.00000  3.14816833    1999_39G8   all_data  NA
#> 1980      39G8 2002     90.00000  3.94785051    2002_39G8   all_data  NA
#> 1981      39G9 1993     99.00000  4.24553493    1993_39G9   all_data  NA
#> 1982      39G9 1994     99.00000  3.01737739    1994_39G9   all_data  NA
#> 1983      39G9 1995     99.00000  2.62102809    1995_39G9   all_data  NA
#> 1984      39G9 1996     99.00000  4.39815711    1996_39G9   all_data  NA
#> 1985      39G9 1997     99.00000  2.00703022    1997_39G9   all_data  NA
#> 1986      39G9 1998     99.00000  1.47906916    1998_39G9   all_data  NA
#> 1987      39G9 1999     99.00000  1.19346156    1999_39G9   all_data  NA
#> 1988      39G9 2000     99.00000  1.36701571    2000_39G9   all_data  NA
#> 1989      39G9 2001     99.00000  2.07158100    2001_39G9   all_data  NA
#> 1990      39G9 2002     99.00000  2.86535853    2002_39G9   all_data  NA
#> 1991      39G9 2009     99.00000  2.33038809    2009_39G9   all_data  NA
#> 1992      39G9 2012     99.00000  2.76619754    2012_39G9   all_data  NA
#> 1993      39G9 2013     99.00000  2.65258806    2013_39G9   all_data  NA
#> 1994      39G9 2019     99.00000  3.43805253    2019_39G9   all_data  NA
#> 1995      39H0 1993     28.00000  7.66373752    1993_39H0   all_data  NA
#> 1996      39H0 1994     28.00000  7.53838912    1994_39H0   all_data  NA
#> 1997      39H0 1995     28.00000  7.46610932    1995_39H0   all_data  NA
#> 1998      39H0 1996     28.00000  7.80617631    1996_39H0   all_data  NA
#> 1999      39H0 1997     28.00000  7.24553021    1997_39H0   all_data  NA
#> 2000      39H0 1998     28.00000  7.61357032    1998_39H0   all_data  NA
#> 2001      39H0 1999     28.00000  7.71378980    1999_39H0   all_data  NA
#> 2002      39H0 2000     28.00000  7.55370793    2000_39H0   all_data  NA
#> 2003      39H0 2001     28.00000  7.63957830    2001_39H0   all_data  NA
#> 2004      39H0 2002     28.00000  7.69008934    2002_39H0   all_data  NA
#> 2005      39H0 2012     28.00000  7.41403360    2012_39H0   all_data  NA
#> 2006      39H0 2013     28.00000  7.55982960    2013_39H0   all_data  NA
#> 2007      39H0 2014     28.00000  7.41652280    2014_39H0   all_data  NA
#> 2008      39H0 2018     28.00000  7.16969025    2018_39H0   all_data  NA
#> 2009      39H1 1993     16.94447  7.92848003    1993_39H1   all_data  NA
#> 2010      39H1 1994     16.94447  6.76520130    1994_39H1   all_data  NA
#> 2011      39H1 1995     16.94447  6.84790373    1995_39H1   all_data  NA
#> 2012      39H1 1996     16.94447  6.30069030    1996_39H1   all_data  NA
#> 2013      39H1 1997     16.94447  7.15760498    1997_39H1   all_data  NA
#> 2014      39H1 1998     16.94447  7.71061296    1998_39H1   all_data  NA
#> 2015      39H1 1999     16.94447  6.64860804    1999_39H1   all_data  NA
#> 2016      39H1 2000     16.94447  6.88042867    2000_39H1   all_data  NA
#> 2017      39H1 2001     16.94447  7.28687867    2001_39H1   all_data  NA
#> 2018      39H1 2002     16.94447  7.45305159    2002_39H1   all_data  NA
#> 2019      39H1 2003     16.94447  7.22602350    2003_39H1   all_data  NA
#> 2020      39H1 2004     16.94447  7.09008829    2004_39H1   all_data  NA
#> 2021      39H1 2005     16.94447  6.83502359    2005_39H1   all_data  NA
#> 2022      39H1 2006     16.94447  6.97542183    2006_39H1   all_data  NA
#> 2023      39H1 2007     16.94447  6.89413450    2007_39H1   all_data  NA
#> 2024      39H1 2008     16.94447  7.13092141    2008_39H1   all_data  NA
#> 2025      39H1 2009     16.94447  7.63121452    2009_39H1   all_data  NA
#> 2026      39H1 2010     16.94447  7.66135109    2010_39H1   all_data  NA
#> 2027      39H1 2011     16.94447  7.35640886    2011_39H1   all_data  NA
#> 2028      39H1 2012     16.94447  7.51877304    2012_39H1   all_data  NA
#> 2029      39H1 2013     16.94447  7.23910587    2013_39H1   all_data  NA
#> 2030      39H1 2014     16.94447  7.06062064    2014_39H1   all_data  NA
#> 2031      39H1 2015     16.94447  7.26020262    2015_39H1   all_data  NA
#> 2032      39H1 2016     16.94447  7.85906982    2016_39H1   all_data  NA
#> 2033      39H1 2017     16.94447  7.95343597    2017_39H1   all_data  NA
#> 2034      39H1 2018     16.94447  7.17325372    2018_39H1   all_data  NA
#> 2035      39H1 2019     16.94447  7.63963868    2019_39H1   all_data  NA
#> 2036      40G8 1993     97.98204  4.72147136    1993_40G8   all_data  NA
#> 2037      40G8 1996     97.98204  4.13542295    1996_40G8   all_data  NA
#> 2038      40G8 1998     97.98204  2.41018887    1998_40G8   all_data  NA
#> 2039      40G8 1999     97.98204  2.21819527    1999_40G8   all_data  NA
#> 2040      40G8 2000     97.98204  2.57848526    2000_40G8   all_data  NA
#> 2041      40G8 2002     97.98204  2.98772334    2002_40G8   all_data  NA
#> 2042      40G8 2003     97.98204  3.44267116    2003_40G8   all_data  NA
#> 2043      40G8 2010     97.98204  2.33200149    2010_40G8   all_data  NA
#> 2044      40G8 2012     97.98204  3.19110875    2012_40G8   all_data  NA
#> 2045      40G8 2018     97.98204  2.89867230    2018_40G8   all_data  NA
#> 2046      40G9 1993     83.07490  5.49383162    1993_40G9   all_data  NA
#> 2047      40G9 1994     83.07490  4.10045274    1994_40G9   all_data  NA
#> 2048      40G9 1995     83.07490  4.44485057    1995_40G9   all_data  NA
#> 2049      40G9 1998     83.07490  2.53299253    1998_40G9   all_data  NA
#> 2050      40G9 1999     83.07490  2.39334762    1999_40G9   all_data  NA
#> 2051      40G9 2001     83.07490  2.85973694    2001_40G9   all_data  NA
#> 2052      40G9 2002     83.07490  3.36717385    2002_40G9   all_data  NA
#> 2053      40G9 2006     83.07490  3.49360473    2006_40G9   all_data  NA
#> 2054      40G9 2009     83.07490  2.96704744    2009_40G9   all_data  NA
#> 2055      40G9 2011     83.07490  2.25333587    2011_40G9   all_data  NA
#> 2056      40G9 2012     83.07490  3.21750024    2012_40G9   all_data  NA
#> 2057      40G9 2013     83.07490  3.56934926    2013_40G9   all_data  NA
#> 2058      40G9 2014     83.07490  3.43339813    2014_40G9   all_data  NA
#> 2059      40G9 2015     83.07490  3.94684700    2015_40G9   all_data  NA
#> 2060      40G9 2018     83.07490  3.20259478    2018_40G9   all_data  NA
#> 2061      40G9 2019     83.07490  3.64144684    2019_40G9   all_data  NA
#> 2062      40H0 1993     44.00000  7.17913771    1993_40H0   all_data  NA
#> 2063      40H0 1994     44.00000  7.24683351    1994_40H0   all_data  NA
#> 2064      40H0 1995     44.00000  7.22433971    1995_40H0   all_data  NA
#> 2065      40H0 1996     44.00000  7.62571470    1996_40H0   all_data  NA
#> 2066      40H0 1997     44.00000  7.39709618    1997_40H0   all_data  NA
#> 2067      40H0 1998     44.00000  7.40917676    1998_40H0   all_data  NA
#> 2068      40H0 1999     44.00000  7.56059886    1999_40H0   all_data  NA
#> 2069      40H0 2000     44.00000  7.34911329    2000_40H0   all_data  NA
#> 2070      40H0 2001     44.00000  7.41942531    2001_40H0   all_data  NA
#> 2071      40H0 2002     44.00000  7.67278711    2002_40H0   all_data  NA
#> 2072      40H1 1993     13.18982  7.44742885    1993_40H1   all_data  NA
#> 2073      40H1 1994     13.18982  7.23045131    1994_40H1   all_data  NA
#> 2074      40H1 1995     13.18982  7.23908378    1995_40H1   all_data  NA
#> 2075      40H1 1996     13.18982  7.67636949    1996_40H1   all_data  NA
#> 2076      40H1 1997     13.18982  7.18711157    1997_40H1   all_data  NA
#> 2077      40H1 1998     13.18982  7.57713233    1998_40H1   all_data  NA
#> 2078      40H1 1999     13.18982  7.48610539    1999_40H1   all_data  NA
#> 2079      40H1 2000     13.18982  7.19185509    2000_40H1   all_data  NA
#> 2080      40H1 2001     13.18982  7.37156771    2001_40H1   all_data  NA
#> 2081      40H1 2002     13.18982  7.59728574    2002_40H1   all_data  NA
#> 2082      40H1 2003     13.18982  7.12506958    2003_40H1   all_data  NA
#> 2083      40H1 2004     13.18982  7.65046426    2004_40H1   all_data  NA
#> 2084      40H1 2005     13.18982  7.27986552    2005_40H1   all_data  NA
#> 2085      40H1 2006     13.18982  7.14262932    2006_40H1   all_data  NA
#> 2086      40H1 2007     13.18982  7.15018764    2007_40H1   all_data  NA
#> 2087      40H1 2008     13.18982  7.48599405    2008_40H1   all_data  NA
#> 2088      40H1 2009     13.18982  7.46993707    2009_40H1   all_data  NA
#> 2089      40H1 2010     13.18982  7.60644904    2010_40H1   all_data  NA
#> 2090      40H1 2011     13.18982  7.31931898    2011_40H1   all_data  NA
#> 2091      40H1 2012     13.18982  7.38152185    2012_40H1   all_data  NA
#> 2092      40H1 2013     13.18982  7.44941703    2013_40H1   all_data  NA
#> 2093      40H1 2014     13.18982  7.51954068    2014_40H1   all_data  NA
#> 2094      40H1 2015     13.18982  7.33573727    2015_40H1   all_data  NA
#> 2095      40H1 2016     13.18982  7.74778505    2016_40H1   all_data  NA
#> 2096      40H1 2017     13.18982  7.47806416    2017_40H1   all_data  NA
#> 2097      40H1 2018     13.18982  7.18133051    2018_40H1   all_data  NA
#> 2098      40H1 2019     13.18982  7.38656169    2019_40H1   all_data  NA
#> 2099      41G4 1993     29.22442  7.66056021    1993_41G4   all_data  NA
#> 2100      41G4 1994     29.22442  7.45234631    1994_41G4   all_data  NA
#> 2101      41G4 1995     29.22442  6.94411951    1995_41G4   all_data  NA
#> 2102      41G4 1996     29.22442  7.21988025    1996_41G4   all_data  NA
#> 2103      41G4 1997     29.22442  7.62431948    1997_41G4   all_data  NA
#> 2104      41G4 1998     29.22442  7.70125693    1998_41G4   all_data  NA
#> 2105      41G4 1999     29.22442  7.69384207    1999_41G4   all_data  NA
#> 2106      41G4 2000     29.22442  6.91988984    2000_41G4   all_data  NA
#> 2107      41G4 2001     29.22442  7.29063214    2001_41G4   all_data  NA
#> 2108      41G4 2002     29.22442  7.58115988    2002_41G4   all_data  NA
#> 2109      41G4 2003     29.22442  7.55986832    2003_41G4   all_data  NA
#> 2110      41G4 2004     29.22442  7.77263941    2004_41G4   all_data  NA
#> 2111      41G4 2005     29.22442  7.53867894    2005_41G4   all_data  NA
#> 2112      41G4 2006     29.22442  7.10086731    2006_41G4   all_data  NA
#> 2113      41G4 2007     29.22442  7.23592604    2007_41G4   all_data  NA
#> 2114      41G4 2008     29.22442  7.20942611    2008_41G4   all_data  NA
#> 2115      41G4 2009     29.22442  7.83967088    2009_41G4   all_data  NA
#> 2116      41G4 2010     29.22442  7.57369406    2010_41G4   all_data  NA
#> 2117      41G4 2011     29.22442  7.70051496    2011_41G4   all_data  NA
#> 2118      41G4 2012     29.22442  7.41120771    2012_41G4   all_data  NA
#> 2119      41G4 2013     29.22442  7.08645872    2013_41G4   all_data  NA
#> 2120      41G4 2014     29.22442  7.31129774    2014_41G4   all_data  NA
#> 2121      41G4 2015     29.22442  7.27558323    2015_41G4   all_data  NA
#> 2122      41G4 2016     29.22442  7.21916562    2016_41G4   all_data  NA
#> 2123      41G4 2017     29.22442  7.23886359    2017_41G4   all_data  NA
#> 2124      41G4 2018     29.22442  7.75843358    2018_41G4   all_data  NA
#> 2125      41G4 2019     29.22442  7.71037704    2019_41G4   all_data  NA
#> 2126      41G5 1993     34.39299  7.76013927    1993_41G5   all_data  NA
#> 2127      41G5 1994     34.39299  7.58057826    1994_41G5   all_data  NA
#> 2128      41G5 1995     34.39299  7.19083529    1995_41G5   all_data  NA
#> 2129      41G5 1996     34.39299  7.50230180    1996_41G5   all_data  NA
#> 2130      41G5 1997     34.39299  7.89047207    1997_41G5   all_data  NA
#> 2131      41G5 1998     34.39299  7.75781746    1998_41G5   all_data  NA
#> 2132      41G5 1999     34.39299  7.89821806    1999_41G5   all_data  NA
#> 2133      41G5 2000     34.39299  7.35182425    2000_41G5   all_data  NA
#> 2134      41G5 2001     34.39299  7.53732334    2001_41G5   all_data  NA
#> 2135      41G5 2002     34.39299  7.75094648    2002_41G5   all_data  NA
#> 2136      41G5 2003     34.39299  7.74432544    2003_41G5   all_data  NA
#> 2137      41G5 2004     34.39299  7.66013497    2004_41G5   all_data  NA
#> 2138      41G5 2005     34.39299  8.05143985    2005_41G5   all_data  NA
#> 2139      41G5 2006     34.39299  7.42255899    2006_41G5   all_data  NA
#> 2140      41G5 2007     34.39299  7.46013589    2007_41G5   all_data  NA
#> 2141      41G5 2008     34.39299  7.30024381    2008_41G5   all_data  NA
#> 2142      41G5 2009     34.39299  7.82440918    2009_41G5   all_data  NA
#> 2143      41G5 2010     34.39299  7.57815225    2010_41G5   all_data  NA
#> 2144      41G5 2011     34.39299  7.07066604    2011_41G5   all_data  NA
#> 2145      41G5 2012     34.39299  7.17843880    2012_41G5   all_data  NA
#> 2146      41G5 2013     34.39299  7.07767122    2013_41G5   all_data  NA
#> 2147      41G5 2014     34.39299  7.12154771    2014_41G5   all_data  NA
#> 2148      41G5 2015     34.39299  7.14734944    2015_41G5   all_data  NA
#> 2149      41G5 2016     34.39299  7.13405120    2016_41G5   all_data  NA
#> 2150      41G5 2017     34.39299  7.13528823    2017_41G5   all_data  NA
#> 2151      41G5 2018     34.39299  7.74903899    2018_41G5   all_data  NA
#> 2152      41G5 2019     34.39299  7.41379322    2019_41G5   all_data  NA
#> 2153      41G6 1993     32.27464  7.65958786    1993_41G6   all_data  NA
#> 2154      41G6 1994     32.27464  7.35507010    1994_41G6   all_data  NA
#> 2155      41G6 1995     32.27464  7.28047796    1995_41G6   all_data  NA
#> 2156      41G6 1996     32.27464  7.22587455    1996_41G6   all_data  NA
#> 2157      41G6 1997     32.27464  7.75848151    1997_41G6   all_data  NA
#> 2158      41G6 1998     32.27464  7.26127803    1998_41G6   all_data  NA
#> 2159      41G6 1999     32.27464  7.61267123    1999_41G6   all_data  NA
#> 2160      41G6 2000     32.27464  7.30705022    2000_41G6   all_data  NA
#> 2161      41G6 2001     32.27464  7.60571260    2001_41G6   all_data  NA
#> 2162      41G6 2002     32.27464  7.70979607    2002_41G6   all_data  NA
#> 2163      41G6 2004     32.27464  7.30891457    2004_41G6   all_data  NA
#> 2164      41G6 2010     32.27464  7.10978431    2010_41G6   all_data  NA
#> 2165      41G6 2011     32.27464  6.22970988    2011_41G6   all_data  NA
#> 2166      41G6 2012     32.27464  7.15040203    2012_41G6   all_data  NA
#> 2167      41G6 2013     32.27464  7.18952819    2013_41G6   all_data  NA
#> 2168      41G6 2015     32.27464  6.89059324    2015_41G6   all_data  NA
#> 2169      41G6 2017     32.27464  6.77858231    2017_41G6   all_data  NA
#> 2170      41G6 2018     32.27464  7.67497859    2018_41G6   all_data  NA
#> 2171      41G6 2019     32.27464  7.12551594    2019_41G6   all_data  NA
#> 2172      41G7 2010     38.00000  6.23922185    2010_41G7   all_data  NA
#> 2173      41G8 1993     96.71104  4.41267478    1993_41G8   all_data  NA
#> 2174      41G8 2001     96.71104  1.17999848    2001_41G8   all_data  NA
#> 2175      41G8 2002     96.71104  1.96729045    2002_41G8   all_data  NA
#> 2176      41G8 2018     96.71104  1.33477489    2018_41G8   all_data  NA
#> 2177      41G9 1993    123.90523  3.73860820    1993_41G9   all_data  NA
#> 2178      41G9 1994    123.90523  2.76994686    1994_41G9   all_data  NA
#> 2179      41G9 1995    123.90523  2.60538030    1995_41G9   all_data  NA
#> 2180      41G9 1996    123.90523  4.18253988    1996_41G9   all_data  NA
#> 2181      41G9 1998    123.90523  1.61088836    1998_41G9   all_data  NA
#> 2182      41G9 1999    123.90523  1.65261641    1999_41G9   all_data  NA
#> 2183      41G9 2001    123.90523  2.37515211    2001_41G9   all_data  NA
#> 2184      41G9 2002    123.90523  2.98152141    2002_41G9   all_data  NA
#> 2185      41G9 2004    123.90523  3.10913151    2004_41G9   all_data  NA
#> 2186      41G9 2007    123.90523  1.15600250    2007_41G9   all_data  NA
#> 2187      41G9 2009    123.90523  2.43754062    2009_41G9   all_data  NA
#> 2188      41G9 2013    123.90523  2.08197150    2013_41G9   all_data  NA
#> 2189      41G9 2019    123.90523  3.47091402    2019_41G9   all_data  NA
#> 2190      41H0 1993     28.04549  7.53692190    1993_41H0   all_data  NA
#> 2191      41H0 1994     28.04549  7.54643308    1994_41H0   all_data  NA
#> 2192      41H0 1995     28.04549  7.49984819    1995_41H0   all_data  NA
#> 2193      41H0 1996     28.04549  7.78563904    1996_41H0   all_data  NA
#> 2194      41H0 1997     28.04549  7.52148318    1997_41H0   all_data  NA
#> 2195      41H0 1998     28.04549  7.56308856    1998_41H0   all_data  NA
#> 2196      41H0 2001     28.04549  7.74257756    2001_41H0   all_data  NA
#> 2197      41H0 2002     28.04549  7.91604973    2002_41H0   all_data  NA
#> 2198      41H0 2003     28.04549  7.27388825    2003_41H0   all_data  NA
#> 2199      41H0 2004     28.04549  7.51453518    2004_41H0   all_data  NA
#> 2200      41H0 2005     28.04549  7.54628763    2005_41H0   all_data  NA
#> 2201      41H0 2006     28.04549  7.49715391    2006_41H0   all_data  NA
#> 2202      41H0 2007     28.04549  7.40963896    2007_41H0   all_data  NA
#> 2203      41H0 2009     28.04549  7.51369732    2009_41H0   all_data  NA
#> 2204      41H0 2013     28.04549  7.74939701    2013_41H0   all_data  NA
#> 2205      41H0 2014     28.04549  7.60618633    2014_41H0   all_data  NA
#> 2206      41H0 2015     28.04549  7.38449543    2015_41H0   all_data  NA
#> 2207      41H0 2019     28.04549  7.33867377    2019_41H0   all_data  NA
#> 2208      41H1 1993     11.11676  7.67226039    1993_41H1   all_data  NA
#> 2209      41H1 1994     11.11676  7.40334892    1994_41H1   all_data  NA
#> 2210      41H1 1995     11.11676  7.34137785    1995_41H1   all_data  NA
#> 2211      41H1 1996     11.11676  7.89110937    1996_41H1   all_data  NA
#> 2212      41H1 1997     11.11676  7.29637583    1997_41H1   all_data  NA
#> 2213      41H1 1998     11.11676  7.56440082    1998_41H1   all_data  NA
#> 2214      41H1 1999     11.11676  7.55951397    1999_41H1   all_data  NA
#> 2215      41H1 2000     11.11676  7.22091540    2000_41H1   all_data  NA
#> 2216      41H1 2001     11.11676  7.49940079    2001_41H1   all_data  NA
#> 2217      41H1 2002     11.11676  7.84510655    2002_41H1   all_data  NA
#> 2218      41H1 2003     11.11676  7.19102042    2003_41H1   all_data  NA
#> 2219      41H1 2004     11.11676  7.63260665    2004_41H1   all_data  NA
#> 2220      41H1 2005     11.11676  7.25788373    2005_41H1   all_data  NA
#> 2221      41H1 2006     11.11676  7.23212700    2006_41H1   all_data  NA
#> 2222      41H1 2007     11.11676  7.12188686    2007_41H1   all_data  NA
#> 2223      41H1 2008     11.11676  7.51304572    2008_41H1   all_data  NA
#> 2224      41H1 2009     11.11676  7.43739221    2009_41H1   all_data  NA
#> 2225      41H1 2010     11.11676  7.69574607    2010_41H1   all_data  NA
#> 2226      41H1 2011     11.11676  7.43306156    2011_41H1   all_data  NA
#> 2227      41H1 2012     11.11676  7.49419732    2012_41H1   all_data  NA
#> 2228      41H1 2013     11.11676  7.55566754    2013_41H1   all_data  NA
#> 2229      41H1 2014     11.11676  7.60086473    2014_41H1   all_data  NA
#> 2230      41H1 2015     11.11676  7.45818187    2015_41H1   all_data  NA
#> 2231      41H1 2016     11.11676  7.89068095    2016_41H1   all_data  NA
#> 2232      41H1 2017     11.11676  7.63635804    2017_41H1   all_data  NA
#> 2233      41H1 2018     11.11676  7.36072421    2018_41H1   all_data  NA
#> 2234      41H1 2019     11.11676  7.49874547    2019_41H1   all_data  NA
#> 2235      42G6 1993     29.09908  7.73389630    1993_42G6   all_data  NA
#> 2236      42G6 1994     29.09908  7.42195232    1994_42G6   all_data  NA
#> 2237      42G6 1995     29.09908  7.50306251    1995_42G6   all_data  NA
#> 2238      42G6 1996     29.09908  7.01549591    1996_42G6   all_data  NA
#> 2239      42G6 1997     29.09908  7.60542062    1997_42G6   all_data  NA
#> 2240      42G6 1998     29.09908  7.61993613    1998_42G6   all_data  NA
#> 2241      42G6 1999     29.09908  7.58934345    1999_42G6   all_data  NA
#> 2242      42G6 2000     29.09908  7.35193473    2000_42G6   all_data  NA
#> 2243      42G6 2004     29.09908  7.91552127    2004_42G6   all_data  NA
#> 2244      42G6 2005     29.09908  7.87826817    2005_42G6   all_data  NA
#> 2245      42G6 2012     29.09908  7.52284180    2012_42G6   all_data  NA
#> 2246      42G6 2013     29.09908  7.34800517    2013_42G6   all_data  NA
#> 2247      42G6 2015     29.09908  7.09332054    2015_42G6   all_data  NA
#> 2248      42G7 1993     79.04017  5.30827735    1993_42G7   all_data  NA
#> 2249      42G7 1994     79.04017  2.64723813    1994_42G7   all_data  NA
#> 2250      42G7 1995     79.04017  3.99965039    1995_42G7   all_data  NA
#> 2251      42G7 1996     79.04017  1.87000687    1996_42G7   all_data  NA
#> 2252      42G7 2003     79.04017  1.29609002    2003_42G7   all_data  NA
#> 2253      42G7 2004     79.04017  2.41048636    2004_42G7   all_data  NA
#> 2254      42G7 2005     79.04017  3.82369308    2005_42G7   all_data  NA
#> 2255      42G7 2006     79.04017  0.94196275    2006_42G7   all_data  NA
#> 2256      42G7 2008     79.04017  1.85699820    2008_42G7   all_data  NA
#> 2257      42G7 2010     79.04017  2.87824646    2010_42G7   all_data  NA
#> 2258      42G7 2013     79.04017  0.62550888    2013_42G7   all_data  NA
#> 2259      42G7 2015     79.04017  1.42330132    2015_42G7   all_data  NA
#> 2260      42G7 2017     79.04017  0.90914205    2017_42G7   all_data  NA
#> 2261      42G8 1993     36.38828  7.83322641    1993_42G8   all_data  NA
#> 2262      42G8 1994     36.38828  8.04074584    1994_42G8   all_data  NA
#> 2263      42G8 1995     36.38828  7.80998146    1995_42G8   all_data  NA
#> 2264      42G8 1996     36.38828  7.62805369    1996_42G8   all_data  NA
#> 2265      42G8 1997     36.38828  7.99604491    1997_42G8   all_data  NA
#> 2266      42G8 1998     36.38828  7.64342145    1998_42G8   all_data  NA
#> 2267      42G8 1999     36.38828  7.80214805    1999_42G8   all_data  NA
#> 2268      42G8 2000     36.38828  7.49862812    2000_42G8   all_data  NA
#> 2269      42G8 2001     36.38828  7.99254410    2001_42G8   all_data  NA
#> 2270      42G8 2002     36.38828  7.83644464    2002_42G8   all_data  NA
#> 2271      42G8 2003     36.38828  7.82224300    2003_42G8   all_data  NA
#> 2272      42G8 2004     36.38828  7.67659323    2004_42G8   all_data  NA
#> 2273      42G8 2005     36.38828  7.84528715    2005_42G8   all_data  NA
#> 2274      42G8 2006     36.38828  7.30247150    2006_42G8   all_data  NA
#> 2275      42G8 2007     36.38828  7.71226262    2007_42G8   all_data  NA
#> 2276      42G8 2008     36.38828  7.45872692    2008_42G8   all_data  NA
#> 2277      42G8 2009     36.38828  7.43115545    2009_42G8   all_data  NA
#> 2278      42G8 2010     36.38828  7.74884506    2010_42G8   all_data  NA
#> 2279      42G8 2011     36.38828  7.36274559    2011_42G8   all_data  NA
#> 2280      42G8 2012     36.38828  7.26829466    2012_42G8   all_data  NA
#> 2281      42G8 2013     36.38828  7.60170998    2013_42G8   all_data  NA
#> 2282      42G8 2014     36.38828  7.42341723    2014_42G8   all_data  NA
#> 2283      42G8 2015     36.38828  7.30951033    2015_42G8   all_data  NA
#> 2284      42G8 2016     36.38828  7.47031058    2016_42G8   all_data  NA
#> 2285      42G8 2017     36.38828  6.85864597    2017_42G8   all_data  NA
#> 2286      42G8 2018     36.38828  7.55873606    2018_42G8   all_data  NA
#> 2287      42G8 2019     36.38828  7.42588358    2019_42G8   all_data  NA
#> 2288      42G9 1993    179.00230 -0.21352503    1993_42G9   all_data  NA
#> 2289      42G9 1994    179.00230  0.51030758    1994_42G9   all_data  NA
#> 2290      42G9 1995    179.00230 -0.34526808    1995_42G9   all_data  NA
#> 2291      42G9 1996    179.00230 -0.29707984    1996_42G9   all_data  NA
#> 2292      42G9 1997    179.00230 -0.38342595    1997_42G9   all_data  NA
#> 2293      42G9 1998    179.00230 -0.21528312    1998_42G9   all_data  NA
#> 2294      42G9 1999    179.00230 -0.69655762    1999_42G9   all_data  NA
#> 2295      42G9 2000    179.00230 -2.39074087    2000_42G9   all_data  NA
#> 2296      42G9 2001    179.00230 -2.22052172    2001_42G9   all_data  NA
#> 2297      42G9 2002    179.00230 -2.28917644    2002_42G9   all_data  NA
#> 2298      42G9 2003    179.00230 -0.61435996    2003_42G9   all_data  NA
#> 2299      42G9 2004    179.00230 -0.53995784    2004_42G9   all_data  NA
#> 2300      42G9 2005    179.00230 -1.23835560    2005_42G9   all_data  NA
#> 2301      42G9 2006    179.00230 -0.97308776    2006_42G9   all_data  NA
#> 2302      42G9 2007    179.00230 -1.46781806    2007_42G9   all_data  NA
#> 2303      42G9 2008    179.00230 -1.62227181    2008_42G9   all_data  NA
#> 2304      42G9 2009    179.00230 -1.88283012    2009_42G9   all_data  NA
#> 2305      42G9 2010    179.00230 -1.78089556    2010_42G9   all_data  NA
#> 2306      42G9 2011    179.00230 -2.43109821    2011_42G9   all_data  NA
#> 2307      42G9 2012    179.00230 -2.38501055    2012_42G9   all_data  NA
#> 2308      42G9 2013    179.00230 -2.10965908    2013_42G9   all_data  NA
#> 2309      42G9 2014    179.00230 -0.59403330    2014_42G9   all_data  NA
#> 2310      42G9 2015    179.00230 -0.49015185    2015_42G9   all_data  NA
#> 2311      42G9 2016    179.00230 -0.16466085    2016_42G9   all_data  NA
#> 2312      42G9 2017    179.00230 -0.49548829    2017_42G9   all_data  NA
#> 2313      42G9 2018    179.00230 -1.25204475    2018_42G9   all_data  NA
#> 2314      42G9 2019    179.00230 -0.56669399    2019_42G9   all_data  NA
#> 2315      42H0 1993     57.59084  7.44341531    1993_42H0   all_data  NA
#> 2316      42H0 1994     57.59084  7.29781040    1994_42H0   all_data  NA
#> 2317      42H0 1995     57.59084  7.20050707    1995_42H0   all_data  NA
#> 2318      42H0 1996     57.59084  7.76952950    1996_42H0   all_data  NA
#> 2319      42H0 1998     57.59084  7.00731055    1998_42H0   all_data  NA
#> 2320      42H1 1993     18.30025  7.98388131    1993_42H1   all_data  NA
#> 2321      42H1 1994     18.30025  7.73413010    1994_42H1   all_data  NA
#> 2322      42H1 1995     18.30025  7.53345711    1995_42H1   all_data  NA
#> 2323      42H1 1996     18.30025  7.94023218    1996_42H1   all_data  NA
#> 2324      42H1 1997     18.30025  7.45455444    1997_42H1   all_data  NA
#> 2325      42H1 1998     18.30025  7.78536054    1998_42H1   all_data  NA
#> 2326      42H1 1999     18.30025  7.65605818    1999_42H1   all_data  NA
#> 2327      42H1 2000     18.30025  7.38661038    2000_42H1   all_data  NA
#> 2328      42H1 2001     18.30025  7.68775677    2001_42H1   all_data  NA
#> 2329      42H1 2002     18.30025  8.01096340    2002_42H1   all_data  NA
#> 2330      42H1 2003     18.30025  7.37061505    2003_42H1   all_data  NA
#> 2331      42H1 2004     18.30025  7.66457465    2004_42H1   all_data  NA
#> 2332      42H1 2005     18.30025  7.50144000    2005_42H1   all_data  NA
#> 2333      42H1 2006     18.30025  7.52218130    2006_42H1   all_data  NA
#> 2334      42H1 2007     18.30025  7.60482170    2007_42H1   all_data  NA
#> 2335      42H1 2008     18.30025  7.71537788    2008_42H1   all_data  NA
#> 2336      42H1 2009     18.30025  7.72670913    2009_42H1   all_data  NA
#> 2337      42H1 2010     18.30025  7.91550677    2010_42H1   all_data  NA
#> 2338      42H1 2011     18.30025  7.61017497    2011_42H1   all_data  NA
#> 2339      42H1 2012     18.30025  7.66729048    2012_42H1   all_data  NA
#> 2340      42H1 2013     18.30025  7.70589821    2013_42H1   all_data  NA
#> 2341      42H1 2014     18.30025  7.65363411    2014_42H1   all_data  NA
#> 2342      42H1 2015     18.30025  7.54550955    2015_42H1   all_data  NA
#> 2343      42H1 2016     18.30025  7.99614969    2016_42H1   all_data  NA
#> 2344      42H1 2017     18.30025  7.79599645    2017_42H1   all_data  NA
#> 2345      42H1 2018     18.30025  7.55210058    2018_42H1   all_data  NA
#> 2346      42H1 2019     18.30025  7.56149286    2019_42H1   all_data  NA
#> 2347      43G6 1993     30.91015  7.59425001    1993_43G6   all_data  NA
#> 2348      43G6 1994     30.91015  6.98145891    1994_43G6   all_data  NA
#> 2349      43G6 1995     30.91015  7.42949538    1995_43G6   all_data  NA
#> 2350      43G6 1996     30.91015  7.09143358    1996_43G6   all_data  NA
#> 2351      43G6 1998     30.91015  6.90988165    1998_43G6   all_data  NA
#> 2352      43G6 2004     30.91015  7.77762803    2004_43G6   all_data  NA
#> 2353      43G6 2013     30.91015  6.91941191    2013_43G6   all_data  NA
#> 2354      43G6 2014     30.91015  6.70009889    2014_43G6   all_data  NA
#> 2355      43G6 2016     30.91015  7.66555512    2016_43G6   all_data  NA
#> 2356      43G6 2017     30.91015  6.90064678    2017_43G6   all_data  NA
#> 2357      43G6 2018     30.91015  7.40607598    2018_43G6   all_data  NA
#> 2358      43G7 1999     83.80202  2.58598322    1999_43G7   all_data  NA
#> 2359      43G7 2003     83.80202  1.39856821    2003_43G7   all_data  NA
#> 2360      43G7 2004     83.80202  2.48957133    2004_43G7   all_data  NA
#> 2361      43G7 2006     83.80202  1.65014790    2006_43G7   all_data  NA
#> 2362      43G7 2012     83.80202  3.17711747    2012_43G7   all_data  NA
#> 2363      43G7 2013     83.80202  1.79516954    2013_43G7   all_data  NA
#> 2364      43G7 2015     83.80202  1.30720808    2015_43G7   all_data  NA
#> 2365      43G8 2001     27.86322  7.98989867    2001_43G8   all_data  NA
#> 2366      43G8 2002     27.86322  7.83590233    2002_43G8   all_data  NA
#> 2367      43G8 2005     27.86322  7.88868175    2005_43G8   all_data  NA
#> 2368      43G9 1996    180.66532  0.72873372    1996_43G9   all_data  NA
#> 2369      43G9 1998    180.66532  0.37476304    1998_43G9   all_data  NA
#> 2370      43G9 2001    180.66532 -0.75326588    2001_43G9   all_data  NA
#> 2371      43G9 2002    180.66532 -0.87102563    2002_43G9   all_data  NA
#> 2372      43G9 2004    180.66532  0.94879775    2004_43G9   all_data  NA
#> 2373      43G9 2006    180.66532 -0.42051901    2006_43G9   all_data  NA
#> 2374      43G9 2012    180.66532 -0.77526161    2012_43G9   all_data  NA
#> 2375      43G9 2014    180.66532  0.22126613    2014_43G9   all_data  NA
#> 2376      43G9 2015    180.66532  0.12570967    2015_43G9   all_data  NA
#> 2377      43G9 2016    180.66532  0.35705679    2016_43G9   all_data  NA
#> 2378      43G9 2019    180.66532  0.12904535    2019_43G9   all_data  NA
#> 2379      43H0 1993    124.04904  6.21293964    1993_43H0   all_data  NA
#> 2380      43H0 1994    124.04904  4.01112435    1994_43H0   all_data  NA
#> 2381      43H0 1995    124.04904  4.14185979    1995_43H0   all_data  NA
#> 2382      43H0 1996    124.04904  5.41035048    1996_43H0   all_data  NA
#> 2383      43H0 2005    124.04904  1.60217509    2005_43H0   all_data  NA
#> 2384      43H0 2006    124.04904  1.84833761    2006_43H0   all_data  NA
#> 2385      43H0 2009    124.04904  2.03411590    2009_43H0   all_data  NA
#> 2386      43H0 2015    124.04904  4.13845068    2015_43H0   all_data  NA
#> 2387      43H1 1993     23.00000  7.70678900    1993_43H1   all_data  NA
#> 2388      43H1 1994     23.00000  7.50578169    1994_43H1   all_data  NA
#> 2389      43H1 1995     23.00000  7.44672206    1995_43H1   all_data  NA
#> 2390      43H1 1996     23.00000  7.92396006    1996_43H1   all_data  NA
#> 2391      43H1 2000     23.00000  7.24706226    2000_43H1   all_data  NA
#> 2392      43H1 2004     23.00000  7.56359968    2004_43H1   all_data  NA
#> 2393      43H1 2007     23.00000  7.47169877    2007_43H1   all_data  NA
#> 2394      43H1 2008     23.00000  7.63645153    2008_43H1   all_data  NA
#> 2395      43H1 2016     23.00000  7.90658215    2016_43H1   all_data  NA
#> 2396      44G6 1993     50.69443  7.68461121    1993_44G6   all_data  NA
#> 2397      44G6 1994     50.69443  7.15367464    1994_44G6   all_data  NA
#> 2398      44G6 1995     50.69443  7.82556761    1995_44G6   all_data  NA
#> 2399      44G6 1996     50.69443  7.68534888    1996_44G6   all_data  NA
#> 2400      44G6 1997     50.69443  7.92742019    1997_44G6   all_data  NA
#> 2401      44G6 1998     50.69443  7.66305466    1998_44G6   all_data  NA
#> 2402      44G6 1999     50.69443  7.54103293    1999_44G6   all_data  NA
#> 2403      44G6 2000     50.69443  7.62919222    2000_44G6   all_data  NA
#> 2404      44G6 2001     50.69443  7.55328329    2001_44G6   all_data  NA
#> 2405      44G6 2002     50.69443  7.86135257    2002_44G6   all_data  NA
#> 2406      44G6 2003     50.69443  7.70986296    2003_44G6   all_data  NA
#> 2407      44G6 2004     50.69443  7.94261685    2004_44G6   all_data  NA
#> 2408      44G6 2005     50.69443  7.79756388    2005_44G6   all_data  NA
#> 2409      44G6 2006     50.69443  6.90588567    2006_44G6   all_data  NA
#> 2410      44G6 2007     50.69443  7.54885020    2007_44G6   all_data  NA
#> 2411      44G6 2008     50.69443  7.04478808    2008_44G6   all_data  NA
#> 2412      44G6 2009     50.69443  8.13603276    2009_44G6   all_data  NA
#> 2413      44G6 2010     50.69443  7.28397441    2010_44G6   all_data  NA
#> 2414      44G6 2011     50.69443  5.91376869    2011_44G6   all_data  NA
#> 2415      44G6 2012     50.69443  7.64334032    2012_44G6   all_data  NA
#> 2416      44G6 2013     50.69443  6.54638622    2013_44G6   all_data  NA
#> 2417      44G6 2014     50.69443  6.45347120    2014_44G6   all_data  NA
#> 2418      44G6 2015     50.69443  5.36760772    2015_44G6   all_data  NA
#> 2419      44G6 2016     50.69443  7.57570168    2016_44G6   all_data  NA
#> 2420      44G6 2017     50.69443  6.76077353    2017_44G6   all_data  NA
#> 2421      44G6 2018     50.69443  7.65040617    2018_44G6   all_data  NA
#> 2422      44G6 2019     50.69443  7.46095143    2019_44G6   all_data  NA
#> 2423      44G7 1993    122.39409  2.53593139    1993_44G7   all_data  NA
#> 2424      44G7 1994    122.39409  1.75851534    1994_44G7   all_data  NA
#> 2425      44G7 1995    122.39409  1.85637850    1995_44G7   all_data  NA
#> 2426      44G7 1996    122.39409  1.33487202    1996_44G7   all_data  NA
#> 2427      44G7 1997    122.39409  1.13249249    1997_44G7   all_data  NA
#> 2428      44G7 1998    122.39409  0.78283806    1998_44G7   all_data  NA
#> 2429      44G7 1999    122.39409  0.49098807    1999_44G7   all_data  NA
#> 2430      44G7 2000    122.39409  0.69738971    2000_44G7   all_data  NA
#> 2431      44G7 2001    122.39409  0.03574116    2001_44G7   all_data  NA
#> 2432      44G7 2002    122.39409  0.42775643    2002_44G7   all_data  NA
#> 2433      44G7 2003    122.39409  0.14073929    2003_44G7   all_data  NA
#> 2434      44G7 2004    122.39409  0.15161280    2004_44G7   all_data  NA
#> 2435      44G7 2005    122.39409  0.51489051    2005_44G7   all_data  NA
#> 2436      44G7 2006    122.39409 -0.35247031    2006_44G7   all_data  NA
#> 2437      44G7 2007    122.39409 -0.23608853    2007_44G7   all_data  NA
#> 2438      44G7 2008    122.39409  0.05998680    2008_44G7   all_data  NA
#> 2439      44G7 2009    122.39409 -0.19124795    2009_44G7   all_data  NA
#> 2440      44G7 2010    122.39409 -0.33215064    2010_44G7   all_data  NA
#> 2441      44G7 2011    122.39409 -0.10581522    2011_44G7   all_data  NA
#> 2442      44G7 2012    122.39409  0.26968857    2012_44G7   all_data  NA
#> 2443      44G7 2013    122.39409  0.32446291    2013_44G7   all_data  NA
#> 2444      44G7 2014    122.39409  0.31990042    2014_44G7   all_data  NA
#> 2445      44G7 2015    122.39409 -0.21167954    2015_44G7   all_data  NA
#> 2446      44G7 2016    122.39409  0.27993611    2016_44G7   all_data  NA
#> 2447      44G7 2017    122.39409 -0.19691194    2017_44G7   all_data  NA
#> 2448      44G7 2018    122.39409  0.18718077    2018_44G7   all_data  NA
#> 2449      44G7 2019    122.39409  1.50757281    2019_44G7   all_data  NA
#> 2450      44G8 1993    110.76247  4.00019389    1993_44G8   all_data  NA
#> 2451      44G8 1995    110.76247  3.17267008    1995_44G8   all_data  NA
#> 2452      44G8 1996    110.76247  2.04626046    1996_44G8   all_data  NA
#> 2453      44G8 1997    110.76247  1.73724744    1997_44G8   all_data  NA
#> 2454      44G8 1998    110.76247  1.74204664    1998_44G8   all_data  NA
#> 2455      44G8 1999    110.76247  1.28251654    1999_44G8   all_data  NA
#> 2456      44G8 2000    110.76247  1.54252815    2000_44G8   all_data  NA
#> 2457      44G8 2001    110.76247  0.87894721    2001_44G8   all_data  NA
#> 2458      44G8 2002    110.76247  1.44048631    2002_44G8   all_data  NA
#> 2459      44G8 2003    110.76247  0.96958954    2003_44G8   all_data  NA
#> 2460      44G8 2004    110.76247  0.56581321    2004_44G8   all_data  NA
#> 2461      44G8 2005    110.76247  1.54673008    2005_44G8   all_data  NA
#> 2462      44G8 2006    110.76247  0.36692755    2006_44G8   all_data  NA
#> 2463      44G8 2007    110.76247  0.65092243    2007_44G8   all_data  NA
#> 2464      44G8 2008    110.76247  0.72267286    2008_44G8   all_data  NA
#> 2465      44G8 2009    110.76247  0.60484651    2009_44G8   all_data  NA
#> 2466      44G8 2010    110.76247  0.74011035    2010_44G8   all_data  NA
#> 2467      44G8 2011    110.76247  0.41831385    2011_44G8   all_data  NA
#> 2468      44G8 2012    110.76247  0.92920334    2012_44G8   all_data  NA
#> 2469      44G8 2013    110.76247  1.20956138    2013_44G8   all_data  NA
#> 2470      44G8 2014    110.76247  1.06620441    2014_44G8   all_data  NA
#> 2471      44G8 2015    110.76247  0.59577904    2015_44G8   all_data  NA
#> 2472      44G8 2016    110.76247  0.87581148    2016_44G8   all_data  NA
#> 2473      44G8 2017    110.76247  0.45060318    2017_44G8   all_data  NA
#> 2474      44G8 2018    110.76247  0.95974925    2018_44G8   all_data  NA
#> 2475      44G8 2019    110.76247  1.33883231    2019_44G8   all_data  NA
#> 2476      44G9 1996     98.95766  2.65524102    1996_44G9   all_data  NA
#> 2477      44G9 2002     98.95766  2.72966988    2002_44G9   all_data  NA
#> 2478      44G9 2003     98.95766  1.91938063    2003_44G9   all_data  NA
#> 2479      44G9 2007     98.95766  1.75243659    2007_44G9   all_data  NA
#> 2480      44G9 2008     98.95766  1.77161590    2008_44G9   all_data  NA
#> 2481      44H0 1993    139.00000  2.53325598    1993_44H0   all_data  NA
#> 2482      44H0 1994    139.00000  1.56110484    1994_44H0   all_data  NA
#> 2483      44H0 1995    139.00000  1.87007058    1995_44H0   all_data  NA
#> 2484      44H0 1996    139.00000  3.41882488    1996_44H0   all_data  NA
#> 2485      44H0 1997    139.00000  2.13308984    1997_44H0   all_data  NA
#> 2486      44H0 1998    139.00000  1.59638851    1998_44H0   all_data  NA
#> 2487      44H0 1999    139.00000  0.54361717    1999_44H0   all_data  NA
#> 2488      44H0 2000    139.00000  0.77099570    2000_44H0   all_data  NA
#> 2489      44H0 2001    139.00000  0.17271389    2001_44H0   all_data  NA
#> 2490      44H0 2002    139.00000  0.32930723    2002_44H0   all_data  NA
#> 2491      44H0 2003    139.00000  0.90104746    2003_44H0   all_data  NA
#> 2492      44H0 2004    139.00000  0.98480765    2004_44H0   all_data  NA
#> 2493      44H0 2005    139.00000  1.12487792    2005_44H0   all_data  NA
#> 2494      44H0 2006    139.00000 -0.03580202    2006_44H0   all_data  NA
#> 2495      44H0 2007    139.00000  0.34600610    2007_44H0   all_data  NA
#> 2496      44H0 2008    139.00000  0.43651244    2008_44H0   all_data  NA
#> 2497      44H0 2011    139.00000 -0.39070845    2011_44H0   all_data  NA
#> 2498      44H0 2012    139.00000 -0.14953050    2012_44H0   all_data  NA
#> 2499      44H0 2013    139.00000  0.80105049    2013_44H0   all_data  NA
#> 2500      44H0 2014    139.00000  1.99644364    2014_44H0   all_data  NA
#> 2501      44H0 2016    139.00000  3.98026624    2016_44H0   all_data  NA
#> 2502      44H0 2017    139.00000  2.45678972    2017_44H0   all_data  NA
#> 2503      44H0 2018    139.00000  0.93826535    2018_44H0   all_data  NA
#> 2504      44H1 1993     30.23161  7.66812010    1993_44H1   all_data  NA
#> 2505      44H1 1994     30.23161  7.48463470    1994_44H1   all_data  NA
#> 2506      44H1 1995     30.23161  7.64794919    1995_44H1   all_data  NA
#> 2507      44H1 1996     30.23161  7.87837719    1996_44H1   all_data  NA
#> 2508      44H1 1999     30.23161  7.83634315    1999_44H1   all_data  NA
#> 2509      44H1 2001     30.23161  7.62386028    2001_44H1   all_data  NA
#> 2510      44H1 2002     30.23161  8.11188027    2002_44H1   all_data  NA
#> 2511      44H1 2003     30.23161  7.45708704    2003_44H1   all_data  NA
#> 2512      44H1 2004     30.23161  7.60755997    2004_44H1   all_data  NA
#> 2513      44H1 2006     30.23161  7.61031516    2006_44H1   all_data  NA
#> 2514      37G2 1993     14.28574  4.20511419    1993_37G2 niche_data  NA
#> 2515      37G2 1994     14.28574  6.63390811    1994_37G2 niche_data  NA
#> 2516      37G2 1995     14.28574  5.96333062    1995_37G2 niche_data  NA
#> 2517      37G2 1996     14.28574  6.17467651    1996_37G2 niche_data  NA
#> 2518      37G2 1997     14.28574  6.30160302    1997_37G2 niche_data  NA
#> 2519      37G2 1998     14.28574  6.68297993    1998_37G2 niche_data  NA
#> 2520      37G2 1999     14.28574  5.75838467    1999_37G2 niche_data  NA
#> 2521      37G2 2000     14.28574  5.27109639    2000_37G2 niche_data  NA
#> 2522      37G2 2001     14.28574  6.09256122    2001_37G2 niche_data  NA
#> 2523      37G2 2004     14.28574  6.04276928    2004_37G2 niche_data  NA
#> 2524      37G2 2007     14.28574  6.29931225    2007_37G2 niche_data  NA
#> 2525      37G2 2018     14.28574  6.57808544    2018_37G2 niche_data  NA
#> 2526      37G3 1993     15.00000  7.76069318    1993_37G3 niche_data  NA
#> 2527      37G3 1995     15.00000  7.17451859    1995_37G3 niche_data  NA
#> 2528      37G3 1996     15.00000  7.15662002    1996_37G3 niche_data  NA
#> 2529      37G3 1997     15.00000  7.23679535    1997_37G3 niche_data  NA
#> 2530      37G3 1998     15.00000  7.85840117    1998_37G3 niche_data  NA
#> 2531      37G3 1999     15.00000  6.86896506    1999_37G3 niche_data  NA
#> 2532      37G3 2000     15.00000  7.12327788    2000_37G3 niche_data  NA
#> 2533      37G3 2001     15.00000  7.20570204    2001_37G3 niche_data  NA
#> 2534      37G3 2002     15.00000  7.56770930    2002_37G3 niche_data  NA
#> 2535      37G3 2003     15.00000  7.22337769    2003_37G3 niche_data  NA
#> 2536      37G3 2007     15.00000  7.28948577    2007_37G3 niche_data  NA
#> 2537      37G3 2017     15.00000  7.67859773    2017_37G3 niche_data  NA
#> 2538      37G4 1994     13.00000  7.58559337    1994_37G4 niche_data  NA
#> 2539      37G4 1995     13.00000  7.39707206    1995_37G4 niche_data  NA
#> 2540      37G4 1996     13.00000  7.53205651    1996_37G4 niche_data  NA
#> 2541      37G4 1998     13.00000  7.83061633    1998_37G4 niche_data  NA
#> 2542      37G4 1999     13.00000  7.44677978    1999_37G4 niche_data  NA
#> 2543      37G4 2000     13.00000  7.48420390    2000_37G4 niche_data  NA
#> 2544      37G4 2001     13.00000  7.45198553    2001_37G4 niche_data  NA
#> 2545      37G4 2003     13.00000  7.32028555    2003_37G4 niche_data  NA
#> 2546      37G4 2010     13.00000  7.83301839    2010_37G4 niche_data  NA
#> 2547      37G5 1993     20.00000  7.60992421    1993_37G5 niche_data  NA
#> 2548      37G5 1994     20.00000  7.48136077    1994_37G5 niche_data  NA
#> 2549      37G5 1995     20.00000  7.34416200    1995_37G5 niche_data  NA
#> 2550      37G5 1996     20.00000  7.61639668    1996_37G5 niche_data  NA
#> 2551      37G5 1997     20.00000  7.29925896    1997_37G5 niche_data  NA
#> 2552      37G5 1998     20.00000  7.65867811    1998_37G5 niche_data  NA
#> 2553      37G5 1999     20.00000  7.53702702    1999_37G5 niche_data  NA
#> 2554      37G5 2000     20.00000  7.47179504    2000_37G5 niche_data  NA
#> 2555      37G5 2001     20.00000  7.45119157    2001_37G5 niche_data  NA
#> 2556      37G5 2006     20.00000  7.30919856    2006_37G5 niche_data  NA
#> 2557      37G6 1993     18.00000  7.49108204    1993_37G6 niche_data  NA
#> 2558      37G6 1994     18.00000  7.39499081    1994_37G6 niche_data  NA
#> 2559      37G6 1995     18.00000  7.27448384    1995_37G6 niche_data  NA
#> 2560      37G6 1996     18.00000  7.74132245    1996_37G6 niche_data  NA
#> 2561      37G6 1997     18.00000  7.22383697    1997_37G6 niche_data  NA
#> 2562      37G6 1998     18.00000  7.71196467    1998_37G6 niche_data  NA
#> 2563      37G6 1999     18.00000  7.64088136    1999_37G6 niche_data  NA
#> 2564      37G6 2000     18.00000  7.39410181    2000_37G6 niche_data  NA
#> 2565      37G6 2001     18.00000  7.62648277    2001_37G6 niche_data  NA
#> 2566      37G6 2002     18.00000  7.54646011    2002_37G6 niche_data  NA
#> 2567      37G6 2004     18.00000  7.68284044    2004_37G6 niche_data  NA
#> 2568      37G6 2005     18.00000  7.47241853    2005_37G6 niche_data  NA
#> 2569      37G6 2006     18.00000  7.46757608    2006_37G6 niche_data  NA
#> 2570      37G6 2007     18.00000  7.28740493    2007_37G6 niche_data  NA
#> 2571      37G6 2015     18.00000  7.55594598    2015_37G6 niche_data  NA
#> 2572      37G6 2016     18.00000  7.75680214    2016_37G6 niche_data  NA
#> 2573      37G8 1993     20.86536  7.01664324    1993_37G8 niche_data  NA
#> 2574      37G8 1994     20.86536  6.15052770    1994_37G8 niche_data  NA
#> 2575      37G8 1995     20.86536  6.34913967    1995_37G8 niche_data  NA
#> 2576      37G8 1996     20.86536  6.53975753    1996_37G8 niche_data  NA
#> 2577      37G8 1997     20.86536  6.51565765    1997_37G8 niche_data  NA
#> 2578      37G8 1998     20.86536  6.23302818    1998_37G8 niche_data  NA
#> 2579      37G8 1999     20.86536  6.44170361    1999_37G8 niche_data  NA
#> 2580      37G8 2000     20.86536  6.26792875    2000_37G8 niche_data  NA
#> 2581      37G8 2001     20.86536  6.82311689    2001_37G8 niche_data  NA
#> 2582      37G8 2002     20.86536  6.88362871    2002_37G8 niche_data  NA
#> 2583      37G8 2003     20.86536  6.64862647    2003_37G8 niche_data  NA
#> 2584      37G8 2004     20.86536  7.13540801    2004_37G8 niche_data  NA
#> 2585      37G8 2005     20.86536  6.98302323    2005_37G8 niche_data  NA
#> 2586      37G8 2006     20.86536  6.88736045    2006_37G8 niche_data  NA
#> 2587      37G8 2008     20.86536  6.92182290    2008_37G8 niche_data  NA
#> 2588      37G8 2009     20.86536  6.63211300    2009_37G8 niche_data  NA
#> 2589      37G8 2010     20.86536  7.28464351    2010_37G8 niche_data  NA
#> 2590      37G8 2011     20.86536  6.58978230    2011_37G8 niche_data  NA
#> 2591      37G8 2012     20.86536  6.71990256    2012_37G8 niche_data  NA
#> 2592      37G8 2013     20.86536  6.93181939    2013_37G8 niche_data  NA
#> 2593      37G8 2016     20.86536  7.20219968    2016_37G8 niche_data  NA
#> 2594      37G8 2017     20.86536  7.08395984    2017_37G8 niche_data  NA
#> 2595      37G8 2018     20.86536  6.72066409    2018_37G8 niche_data  NA
#> 2596      37G8 2019     20.86536  7.00984767    2019_37G8 niche_data  NA
#> 2597      37G9 1993     43.07566  7.06676271    1993_37G9 niche_data  NA
#> 2598      37G9 1994     43.07566  6.19283092    1994_37G9 niche_data  NA
#> 2599      37G9 1995     43.07566  6.52132416    1995_37G9 niche_data  NA
#> 2600      37G9 1996     43.07566  6.79768933    1996_37G9 niche_data  NA
#> 2601      37G9 1997     43.07566  6.16910845    1997_37G9 niche_data  NA
#> 2602      37G9 1998     43.07566  6.08435426    1998_37G9 niche_data  NA
#> 2603      37G9 1999     43.07566  6.38760333    1999_37G9 niche_data  NA
#> 2604      37G9 2000     43.07566  6.36699536    2000_37G9 niche_data  NA
#> 2605      37G9 2001     43.07566  6.68125086    2001_37G9 niche_data  NA
#> 2606      37G9 2002     43.07566  6.91182196    2002_37G9 niche_data  NA
#> 2607      37G9 2014     43.07566  6.70335729    2014_37G9 niche_data  NA
#> 2608      38G2 1993     17.00000  6.05552989    1993_38G2 niche_data  NA
#> 2609      38G2 1994     17.00000  6.82655030    1994_38G2 niche_data  NA
#> 2610      38G2 1995     17.00000  6.78852044    1995_38G2 niche_data  NA
#> 2611      38G2 1996     17.00000  6.85859963    1996_38G2 niche_data  NA
#> 2612      38G2 1997     17.00000  7.05134306    1997_38G2 niche_data  NA
#> 2613      38G2 1998     17.00000  7.32619512    1998_38G2 niche_data  NA
#> 2614      38G5 1993     61.00000  3.96560658    1993_38G5 niche_data  NA
#> 2615      38G5 1994     61.20802  3.20767527    1994_38G5 niche_data  NA
#> 2616      38G5 1995     57.00000  4.17131488    1995_38G5 niche_data  NA
#> 2617      38G5 1996     61.20802  4.82126540    1996_38G5 niche_data  NA
#> 2618      38G5 1997     57.00000  3.63807618    1997_38G5 niche_data  NA
#> 2619      38G5 1998     57.00000  4.37412120    1998_38G5 niche_data  NA
#> 2620      38G5 1999     58.00000  3.74897011    1999_38G5 niche_data  NA
#> 2621      38G5 2001     57.96658  4.25670554    2001_38G5 niche_data  NA
#> 2622      38G5 2002     57.93315  5.05532741    2002_38G5 niche_data  NA
#> 2623      38G6 1993     26.52553  7.44933723    1993_38G6 niche_data  NA
#> 2624      38G6 1994     26.52553  7.39133176    1994_38G6 niche_data  NA
#> 2625      38G6 1995     26.43109  7.21344589    1995_38G6 niche_data  NA
#> 2626      38G6 1996     26.52553  7.64821966    1996_38G6 niche_data  NA
#> 2627      38G6 1997     26.52553  7.23463595    1997_38G6 niche_data  NA
#> 2628      38G6 1998     26.43109  7.59565926    1998_38G6 niche_data  NA
#> 2629      38G6 1999     26.43109  7.59284166    1999_38G6 niche_data  NA
#> 2630      38G6 2001     26.43109  7.54168520    2001_38G6 niche_data  NA
#> 2631      38G7 1993     21.00000  7.66297490    1993_38G7 niche_data  NA
#> 2632      38G7 1994     21.00000  7.62870196    1994_38G7 niche_data  NA
#> 2633      38G7 1995     21.00000  7.34043636    1995_38G7 niche_data  NA
#> 2634      38G7 1996     21.00000  7.72085964    1996_38G7 niche_data  NA
#> 2635      38G7 1997     21.00000  7.50529819    1997_38G7 niche_data  NA
#> 2636      38G7 1998     21.00000  7.75350536    1998_38G7 niche_data  NA
#> 2637      38G7 1999     21.00000  7.75870685    1999_38G7 niche_data  NA
#> 2638      38G7 2000     21.00000  7.48371628    2000_38G7 niche_data  NA
#> 2639      38G7 2001     21.00000  7.69145452    2001_38G7 niche_data  NA
#> 2640      38G7 2003     21.00000  7.39710867    2003_38G7 niche_data  NA
#> 2641      38G7 2006     21.00000  7.51662002    2006_38G7 niche_data  NA
#> 2642      38G8 1993     28.72128  7.12363056    1993_38G8 niche_data  NA
#> 2643      38G8 1994     28.72128  6.97717105    1994_38G8 niche_data  NA
#> 2644      38G8 1995     28.72128  6.75633193    1995_38G8 niche_data  NA
#> 2645      38G8 1996     28.72128  7.10185434    1996_38G8 niche_data  NA
#> 2646      38G8 1997     28.72128  6.74932606    1997_38G8 niche_data  NA
#> 2647      38G8 1998     28.72128  6.59850014    1998_38G8 niche_data  NA
#> 2648      38G8 1999     28.72128  7.14287638    1999_38G8 niche_data  NA
#> 2649      38G8 2000     28.72128  6.61911035    2000_38G8 niche_data  NA
#> 2650      38G8 2001     28.72128  7.11668589    2001_38G8 niche_data  NA
#> 2651      38G8 2002     28.72128  7.13619961    2002_38G8 niche_data  NA
#> 2652      38G9 1993     61.59609  6.53527481    1993_38G9 niche_data  NA
#> 2653      38G9 1994     61.59609  6.32189758    1994_38G9 niche_data  NA
#> 2654      38G9 1995     61.59609  6.66182950    1995_38G9 niche_data  NA
#> 2655      38G9 1996     61.59609  7.57132352    1996_38G9 niche_data  NA
#> 2656      38G9 1997     59.60221  5.02414804    1997_38G9 niche_data  NA
#> 2657      38G9 1998     61.59609  4.90418089    1998_38G9 niche_data  NA
#> 2658      38G9 1999     61.59609  5.38034400    1999_38G9 niche_data  NA
#> 2659      38G9 2000     53.88420  6.14271727    2000_38G9 niche_data  NA
#> 2660      38G9 2001     61.59609  6.33206723    2001_38G9 niche_data  NA
#> 2661      38G9 2002     61.59609  6.50562502    2002_38G9 niche_data  NA
#> 2662      38G9 2009     61.59609  5.79178332    2009_38G9 niche_data  NA
#> 2663      38H0 1993     25.40843  8.11768631    1993_38H0 niche_data  NA
#> 2664      38H0 1994     25.40843  7.73542899    1994_38H0 niche_data  NA
#> 2665      38H0 1995     25.40843  7.57679975    1995_38H0 niche_data  NA
#> 2666      38H0 1996     25.40843  7.50906781    1996_38H0 niche_data  NA
#> 2667      38H0 1997     25.40843  7.30467319    1997_38H0 niche_data  NA
#> 2668      38H0 1998     25.40843  7.90287733    1998_38H0 niche_data  NA
#> 2669      38H0 1999     25.40843  7.73133571    1999_38H0 niche_data  NA
#> 2670      38H0 2000     25.40843  7.48040935    2000_38H0 niche_data  NA
#> 2671      38H0 2001     25.40843  7.75845265    2001_38H0 niche_data  NA
#> 2672      38H0 2002     25.40843  7.77892181    2002_38H0 niche_data  NA
#> 2673      38H0 2003     25.40843  7.81739805    2003_38H0 niche_data  NA
#> 2674      38H0 2004     25.40843  7.68964871    2004_38H0 niche_data  NA
#> 2675      38H0 2005     25.40843  7.44535883    2005_38H0 niche_data  NA
#> 2676      38H0 2006     25.40843  6.99158635    2006_38H0 niche_data  NA
#> 2677      38H0 2007     25.40843  7.60890696    2007_38H0 niche_data  NA
#> 2678      38H0 2008     25.40843  7.63600128    2008_38H0 niche_data  NA
#> 2679      38H0 2009     25.40843  7.76965866    2009_38H0 niche_data  NA
#> 2680      38H0 2010     25.40843  8.06326072    2010_38H0 niche_data  NA
#> 2681      38H0 2011     25.40843  7.75620335    2011_38H0 niche_data  NA
#> 2682      38H0 2012     25.40843  7.90984354    2012_38H0 niche_data  NA
#> 2683      38H0 2013     25.40843  7.65943229    2013_38H0 niche_data  NA
#> 2684      38H0 2014     25.40843  7.50772141    2014_38H0 niche_data  NA
#> 2685      38H0 2015     25.40843  7.86688978    2015_38H0 niche_data  NA
#> 2686      38H0 2016     25.40843  8.25953099    2016_38H0 niche_data  NA
#> 2687      38H0 2017     25.40843  8.07327909    2017_38H0 niche_data  NA
#> 2688      38H0 2018     25.40843  7.98754044    2018_38H0 niche_data  NA
#> 2689      38H0 2019     25.40843  7.97582971    2019_38H0 niche_data  NA
#> 2690      38H1 1993     19.05070  8.07086738    1993_38H1 niche_data  NA
#> 2691      38H1 1994     19.05070  7.39894801    1994_38H1 niche_data  NA
#> 2692      38H1 1995     19.05070  7.16752888    1995_38H1 niche_data  NA
#> 2693      38H1 1996     19.05070  6.93238265    1996_38H1 niche_data  NA
#> 2694      38H1 1997     19.05070  7.28553821    1997_38H1 niche_data  NA
#> 2695      38H1 1998     19.05070  7.80666691    1998_38H1 niche_data  NA
#> 2696      38H1 1999     19.05070  7.29755746    1999_38H1 niche_data  NA
#> 2697      38H1 2000     19.05070  7.11444096    2000_38H1 niche_data  NA
#> 2698      38H1 2001     19.05070  7.46701962    2001_38H1 niche_data  NA
#> 2699      38H1 2002     19.05070  7.74018533    2002_38H1 niche_data  NA
#> 2700      38H1 2003     19.05070  7.72443087    2003_38H1 niche_data  NA
#> 2701      38H1 2004     19.05070  7.25328379    2004_38H1 niche_data  NA
#> 2702      38H1 2005     19.05070  6.97672099    2005_38H1 niche_data  NA
#> 2703      38H1 2006     19.05070  6.90305419    2006_38H1 niche_data  NA
#> 2704      38H1 2007     19.05070  7.49408782    2007_38H1 niche_data  NA
#> 2705      38H1 2008     19.05070  7.48430650    2008_38H1 niche_data  NA
#> 2706      38H1 2009     19.05070  7.72732625    2009_38H1 niche_data  NA
#> 2707      38H1 2010     19.05070  7.92293670    2010_38H1 niche_data  NA
#> 2708      38H1 2011     19.05070  7.74005428    2011_38H1 niche_data  NA
#> 2709      38H1 2012     19.05070  7.87143226    2012_38H1 niche_data  NA
#> 2710      38H1 2013     19.05070  7.78247626    2013_38H1 niche_data  NA
#> 2711      38H1 2014     19.05070  7.50278451    2014_38H1 niche_data  NA
#> 2712      38H1 2015     19.05070  7.84719548    2015_38H1 niche_data  NA
#> 2713      38H1 2016     19.05070  8.26214908    2016_38H1 niche_data  NA
#> 2714      38H1 2017     19.05070  8.06476599    2017_38H1 niche_data  NA
#> 2715      38H1 2018     19.05070  7.96728259    2018_38H1 niche_data  NA
#> 2716      38H1 2019     19.05070  8.01308126    2019_38H1 niche_data  NA
#> 2717      39G2 1993     19.00000  7.29217886    1993_39G2 niche_data  NA
#> 2718      39G2 1994     19.00000  7.08028842    1994_39G2 niche_data  NA
#> 2719      39G2 1995     19.00000  7.12534761    1995_39G2 niche_data  NA
#> 2720      39G2 1996     19.00000  7.23473904    1996_39G2 niche_data  NA
#> 2721      39G2 1997     19.00000  7.52556012    1997_39G2 niche_data  NA
#> 2722      39G2 1998     19.00000  7.44898598    1998_39G2 niche_data  NA
#> 2723      39G2 1999     19.00000  7.23632361    1999_39G2 niche_data  NA
#> 2724      39G2 2001     19.00000  7.73925850    2001_39G2 niche_data  NA
#> 2725      39G2 2003     19.00000  7.30094799    2003_39G2 niche_data  NA
#> 2726      39G2 2004     19.00000  7.31416437    2004_39G2 niche_data  NA
#> 2727      39G2 2005     19.00000  6.86376368    2005_39G2 niche_data  NA
#> 2728      39G2 2009     19.00000  7.25172088    2009_39G2 niche_data  NA
#> 2729      39G2 2010     19.00000  7.57748679    2010_39G2 niche_data  NA
#> 2730      39G2 2012     19.00000  7.53586179    2012_39G2 niche_data  NA
#> 2731      39G5 1994     77.91940  2.21667040    1994_39G5 niche_data  NA
#> 2732      39G5 1995     67.00024  4.09008511    1995_39G5 niche_data  NA
#> 2733      39G5 1997     76.01884  2.50689933    1997_39G5 niche_data  NA
#> 2734      39G5 1998     70.82971  4.28781619    1998_39G5 niche_data  NA
#> 2735      39G5 2000     65.31756  3.97266155    2000_39G5 niche_data  NA
#> 2736      39G5 2001     78.00000  2.95947631    2001_39G5 niche_data  NA
#> 2737      39G5 2014     75.62254  2.26101847    2014_39G5 niche_data  NA
#> 2738      39G6 1993     66.00000  4.71573259    1993_39G6 niche_data  NA
#> 2739      39G6 1994     62.09842  3.88251945    1994_39G6 niche_data  NA
#> 2740      39G6 1995     56.53212  4.74775274    1995_39G6 niche_data  NA
#> 2741      39G6 1996     63.79890  5.04541013    1996_39G6 niche_data  NA
#> 2742      39G6 1998     60.00000  4.54512907    1998_39G6 niche_data  NA
#> 2743      39G6 1999     58.00000  4.54083026    1999_39G6 niche_data  NA
#> 2744      39G6 2001     58.18382  4.61939221    2001_39G6 niche_data  NA
#> 2745      39G6 2002     60.06716  4.57397097    2002_39G6 niche_data  NA
#> 2746      39G7 1993     52.79956  6.67415281    1993_39G7 niche_data  NA
#> 2747      39G7 1994     52.79956  6.55919462    1994_39G7 niche_data  NA
#> 2748      39G7 1995     52.79956  6.40591612    1995_39G7 niche_data  NA
#> 2749      39G7 1996     52.79956  6.98277240    1996_39G7 niche_data  NA
#> 2750      39G7 1997     52.79956  6.36291991    1997_39G7 niche_data  NA
#> 2751      39G7 1998     52.79956  6.10955028    1998_39G7 niche_data  NA
#> 2752      39G7 1999     52.79956  6.39000604    1999_39G7 niche_data  NA
#> 2753      39G7 2001     52.79956  6.58363755    2001_39G7 niche_data  NA
#> 2754      39G8 1993     88.00000  5.06969009    1993_39G8 niche_data  NA
#> 2755      39G8 1994     88.00000  4.08638021    1994_39G8 niche_data  NA
#> 2756      39G8 1995     88.00000  4.31855172    1995_39G8 niche_data  NA
#> 2757      39G8 1996     88.00000  5.13901725    1996_39G8 niche_data  NA
#> 2758      39G8 1997     88.00000  4.44219563    1997_39G8 niche_data  NA
#> 2759      39G8 1998     88.00000  3.74763196    1998_39G8 niche_data  NA
#> 2760      39G8 1999     88.00000  3.29362047    1999_39G8 niche_data  NA
#> 2761      39G8 2002     88.00000  4.12551365    2002_39G8 niche_data  NA
#> 2762      39G9 1993     88.00000  5.21721358    1993_39G9 niche_data  NA
#> 2763      39G9 1994     88.00000  4.26600259    1994_39G9 niche_data  NA
#> 2764      39G9 1995     86.05812  4.94106030    1995_39G9 niche_data  NA
#> 2765      39G9 1996     88.00000  5.29567123    1996_39G9 niche_data  NA
#> 2766      39G9 1997     86.00000  3.53934694    1997_39G9 niche_data  NA
#> 2767      39G9 1998     82.50000  3.51990253    1998_39G9 niche_data  NA
#> 2768      39G9 1999     81.12589  3.51589365    1999_39G9 niche_data  NA
#> 2769      39G9 2000     83.00000  4.04939629    2000_39G9 niche_data  NA
#> 2770      39G9 2001     83.00000  3.60633832    2001_39G9 niche_data  NA
#> 2771      39G9 2002     86.00000  4.55020559    2002_39G9 niche_data  NA
#> 2772      39G9 2009     86.00000  3.69063626    2009_39G9 niche_data  NA
#> 2773      39G9 2012     86.00000  3.89420214    2012_39G9 niche_data  NA
#> 2774      39G9 2013     86.00000  4.42933517    2013_39G9 niche_data  NA
#> 2775      39G9 2019     88.00000  4.39378952    2019_39G9 niche_data  NA
#> 2776      39H0 1993     28.00000  7.66373752    1993_39H0 niche_data  NA
#> 2777      39H0 1994     28.00000  7.53838912    1994_39H0 niche_data  NA
#> 2778      39H0 1995     28.00000  7.46610932    1995_39H0 niche_data  NA
#> 2779      39H0 1996     28.00000  7.80617631    1996_39H0 niche_data  NA
#> 2780      39H0 1997     28.00000  7.24553021    1997_39H0 niche_data  NA
#> 2781      39H0 1998     28.00000  7.61357032    1998_39H0 niche_data  NA
#> 2782      39H0 1999     28.00000  7.71378980    1999_39H0 niche_data  NA
#> 2783      39H0 2000     28.00000  7.55370793    2000_39H0 niche_data  NA
#> 2784      39H0 2001     28.00000  7.63957830    2001_39H0 niche_data  NA
#> 2785      39H0 2002     28.00000  7.69008934    2002_39H0 niche_data  NA
#> 2786      39H0 2012     28.00000  7.41403360    2012_39H0 niche_data  NA
#> 2787      39H0 2013     28.00000  7.55982960    2013_39H0 niche_data  NA
#> 2788      39H0 2014     28.00000  7.41652280    2014_39H0 niche_data  NA
#> 2789      39H0 2018     28.00000  7.16969025    2018_39H0 niche_data  NA
#> 2790      39H1 1993     16.94447  7.92848003    1993_39H1 niche_data  NA
#> 2791      39H1 1994     16.94447  6.76520130    1994_39H1 niche_data  NA
#> 2792      39H1 1995     16.94447  6.84790373    1995_39H1 niche_data  NA
#> 2793      39H1 1996     16.94447  6.30069030    1996_39H1 niche_data  NA
#> 2794      39H1 1997     16.94447  7.15760498    1997_39H1 niche_data  NA
#> 2795      39H1 1998     16.94447  7.71061296    1998_39H1 niche_data  NA
#> 2796      39H1 1999     16.94447  6.64860804    1999_39H1 niche_data  NA
#> 2797      39H1 2000     16.94447  6.88042867    2000_39H1 niche_data  NA
#> 2798      39H1 2001     16.94447  7.28687867    2001_39H1 niche_data  NA
#> 2799      39H1 2002     16.94447  7.45305159    2002_39H1 niche_data  NA
#> 2800      39H1 2003     16.94447  7.22602350    2003_39H1 niche_data  NA
#> 2801      39H1 2004     16.94447  7.09008829    2004_39H1 niche_data  NA
#> 2802      39H1 2005     16.94447  6.83502359    2005_39H1 niche_data  NA
#> 2803      39H1 2006     16.94447  6.97542183    2006_39H1 niche_data  NA
#> 2804      39H1 2007     16.94447  6.89413450    2007_39H1 niche_data  NA
#> 2805      39H1 2008     16.94447  7.13092141    2008_39H1 niche_data  NA
#> 2806      39H1 2009     16.94447  7.63121452    2009_39H1 niche_data  NA
#> 2807      39H1 2010     16.94447  7.66135109    2010_39H1 niche_data  NA
#> 2808      39H1 2011     16.94447  7.35640886    2011_39H1 niche_data  NA
#> 2809      39H1 2012     16.94447  7.51877304    2012_39H1 niche_data  NA
#> 2810      39H1 2013     16.94447  7.23910587    2013_39H1 niche_data  NA
#> 2811      39H1 2014     16.94447  7.06062064    2014_39H1 niche_data  NA
#> 2812      39H1 2015     16.94447  7.26020262    2015_39H1 niche_data  NA
#> 2813      39H1 2016     16.94447  7.85906982    2016_39H1 niche_data  NA
#> 2814      39H1 2017     16.94447  7.95343597    2017_39H1 niche_data  NA
#> 2815      39H1 2018     16.94447  7.17325372    2018_39H1 niche_data  NA
#> 2816      39H1 2019     16.94447  7.63963868    2019_39H1 niche_data  NA
#> 2817      40G8 1993     90.00000  5.18663251    1993_40G8 niche_data  NA
#> 2818      40G8 1996     90.00000  4.73106145    1996_40G8 niche_data  NA
#> 2819      40G8 1998     90.00000  3.13343910    1998_40G8 niche_data  NA
#> 2820      40G8 1999     90.00000  2.65307058    1999_40G8 niche_data  NA
#> 2821      40G8 2000     90.00000  3.25554205    2000_40G8 niche_data  NA
#> 2822      40G8 2002     90.00000  3.45824007    2002_40G8 niche_data  NA
#> 2823      40G8 2003     90.00000  3.88511078    2003_40G8 niche_data  NA
#> 2824      40G8 2010     90.00000  2.70500445    2010_40G8 niche_data  NA
#> 2825      40G8 2012     90.00000  3.70618155    2012_40G8 niche_data  NA
#> 2826      40G8 2018     90.00000  3.49760928    2018_40G8 niche_data  NA
#> 2827      40G9 1993     82.00000  5.54895784    1993_40G9 niche_data  NA
#> 2828      40G9 1994     82.00000  4.27678489    1994_40G9 niche_data  NA
#> 2829      40G9 1995     82.00000  4.56483732    1995_40G9 niche_data  NA
#> 2830      40G9 1998     78.06110  2.91320160    1998_40G9 niche_data  NA
#> 2831      40G9 1999     75.00000  2.94680805    1999_40G9 niche_data  NA
#> 2832      40G9 2001     77.88208  3.05396092    2001_40G9 niche_data  NA
#> 2833      40G9 2002     82.00000  3.41270843    2002_40G9 niche_data  NA
#> 2834      40G9 2006     82.00000  3.51193250    2006_40G9 niche_data  NA
#> 2835      40G9 2009     79.00000  3.14256950    2009_40G9 niche_data  NA
#> 2836      40G9 2011     78.00000  3.23689323    2011_40G9 niche_data  NA
#> 2837      40G9 2012     79.00000  3.45831947    2012_40G9 niche_data  NA
#> 2838      40G9 2013     80.22516  3.68908618    2013_40G9 niche_data  NA
#> 2839      40G9 2014     82.00000  3.38371457    2014_40G9 niche_data  NA
#> 2840      40G9 2015     81.00000  4.11646475    2015_40G9 niche_data  NA
#> 2841      40G9 2018     78.99048  3.40002225    2018_40G9 niche_data  NA
#> 2842      40G9 2019     82.00000  3.64282224    2019_40G9 niche_data  NA
#> 2843      40H0 1993     44.00000  7.17913771    1993_40H0 niche_data  NA
#> 2844      40H0 1994     44.00000  7.24683351    1994_40H0 niche_data  NA
#> 2845      40H0 1995     44.00000  7.22433971    1995_40H0 niche_data  NA
#> 2846      40H0 1996     44.00000  7.62571470    1996_40H0 niche_data  NA
#> 2847      40H0 1997     44.00000  7.39709618    1997_40H0 niche_data  NA
#> 2848      40H0 1998     44.00000  7.40917676    1998_40H0 niche_data  NA
#> 2849      40H0 1999     43.00000  7.57169034    1999_40H0 niche_data  NA
#> 2850      40H0 2000     44.00000  7.34911329    2000_40H0 niche_data  NA
#> 2851      40H0 2001     44.00000  7.41942531    2001_40H0 niche_data  NA
#> 2852      40H0 2002     44.00000  7.67278711    2002_40H0 niche_data  NA
#> 2853      40H1 1993     13.18982  7.44742885    1993_40H1 niche_data  NA
#> 2854      40H1 1994     13.18982  7.23045131    1994_40H1 niche_data  NA
#> 2855      40H1 1995     13.18982  7.23908378    1995_40H1 niche_data  NA
#> 2856      40H1 1996     13.18982  7.67636949    1996_40H1 niche_data  NA
#> 2857      40H1 1997     13.18982  7.18711157    1997_40H1 niche_data  NA
#> 2858      40H1 1998     13.18982  7.57713233    1998_40H1 niche_data  NA
#> 2859      40H1 1999     13.18982  7.48610539    1999_40H1 niche_data  NA
#> 2860      40H1 2000     13.18982  7.19185509    2000_40H1 niche_data  NA
#> 2861      40H1 2001     13.18982  7.37156771    2001_40H1 niche_data  NA
#> 2862      40H1 2002     13.18982  7.59728574    2002_40H1 niche_data  NA
#> 2863      40H1 2003     13.18982  7.12506958    2003_40H1 niche_data  NA
#> 2864      40H1 2004     13.18982  7.65046426    2004_40H1 niche_data  NA
#> 2865      40H1 2005     13.18982  7.27986552    2005_40H1 niche_data  NA
#> 2866      40H1 2006     13.18982  7.14262932    2006_40H1 niche_data  NA
#> 2867      40H1 2007     13.18982  7.15018764    2007_40H1 niche_data  NA
#> 2868      40H1 2008     13.18982  7.48599405    2008_40H1 niche_data  NA
#> 2869      40H1 2009     13.18982  7.46993707    2009_40H1 niche_data  NA
#> 2870      40H1 2010     13.18982  7.60644904    2010_40H1 niche_data  NA
#> 2871      40H1 2011     13.18982  7.31931898    2011_40H1 niche_data  NA
#> 2872      40H1 2012     13.18982  7.38152185    2012_40H1 niche_data  NA
#> 2873      40H1 2013     13.18982  7.44941703    2013_40H1 niche_data  NA
#> 2874      40H1 2014     13.18982  7.51954068    2014_40H1 niche_data  NA
#> 2875      40H1 2015     13.18982  7.33573727    2015_40H1 niche_data  NA
#> 2876      40H1 2016     13.18982  7.74778505    2016_40H1 niche_data  NA
#> 2877      40H1 2017     13.18982  7.47806416    2017_40H1 niche_data  NA
#> 2878      40H1 2018     13.18982  7.18133051    2018_40H1 niche_data  NA
#> 2879      40H1 2019     13.18982  7.38656169    2019_40H1 niche_data  NA
#> 2880      41G4 1993     29.22442  7.66056021    1993_41G4 niche_data  NA
#> 2881      41G4 1994     29.22442  7.45234631    1994_41G4 niche_data  NA
#> 2882      41G4 1995     29.22442  6.94411951    1995_41G4 niche_data  NA
#> 2883      41G4 1996     29.22442  7.21988025    1996_41G4 niche_data  NA
#> 2884      41G4 1997     29.22442  7.62431948    1997_41G4 niche_data  NA
#> 2885      41G4 1998     29.22442  7.70125693    1998_41G4 niche_data  NA
#> 2886      41G4 1999     29.22442  7.69384207    1999_41G4 niche_data  NA
#> 2887      41G4 2000     29.22442  6.91988984    2000_41G4 niche_data  NA
#> 2888      41G4 2001     29.22442  7.29063214    2001_41G4 niche_data  NA
#> 2889      41G4 2002     29.22442  7.58115988    2002_41G4 niche_data  NA
#> 2890      41G4 2003     29.22442  7.55986832    2003_41G4 niche_data  NA
#> 2891      41G4 2004     29.22442  7.77263941    2004_41G4 niche_data  NA
#> 2892      41G4 2005     29.22442  7.53867894    2005_41G4 niche_data  NA
#> 2893      41G4 2006     29.22442  7.10086731    2006_41G4 niche_data  NA
#> 2894      41G4 2007     29.22442  7.23592604    2007_41G4 niche_data  NA
#> 2895      41G4 2008     29.22442  7.20942611    2008_41G4 niche_data  NA
#> 2896      41G4 2009     29.22442  7.83967088    2009_41G4 niche_data  NA
#> 2897      41G4 2010     29.22442  7.57369406    2010_41G4 niche_data  NA
#> 2898      41G4 2011     29.22442  7.70051496    2011_41G4 niche_data  NA
#> 2899      41G4 2012     29.22442  7.41120771    2012_41G4 niche_data  NA
#> 2900      41G4 2013     29.22442  7.08645872    2013_41G4 niche_data  NA
#> 2901      41G4 2014     29.22442  7.31129774    2014_41G4 niche_data  NA
#> 2902      41G4 2015     29.22442  7.27558323    2015_41G4 niche_data  NA
#> 2903      41G4 2016     29.22442  7.21916562    2016_41G4 niche_data  NA
#> 2904      41G4 2017     29.22442  7.23886359    2017_41G4 niche_data  NA
#> 2905      41G4 2018     29.22442  7.75843358    2018_41G4 niche_data  NA
#> 2906      41G4 2019     29.22442  7.71037704    2019_41G4 niche_data  NA
#> 2907      41G5 1993     34.39299  7.76013927    1993_41G5 niche_data  NA
#> 2908      41G5 1994     34.39299  7.58057826    1994_41G5 niche_data  NA
#> 2909      41G5 1995     34.39299  7.19083529    1995_41G5 niche_data  NA
#> 2910      41G5 1996     34.39299  7.50230180    1996_41G5 niche_data  NA
#> 2911      41G5 1997     34.39299  7.89047207    1997_41G5 niche_data  NA
#> 2912      41G5 1998     34.39299  7.75781746    1998_41G5 niche_data  NA
#> 2913      41G5 1999     34.39299  7.89821806    1999_41G5 niche_data  NA
#> 2914      41G5 2000     34.39299  7.35182425    2000_41G5 niche_data  NA
#> 2915      41G5 2001     34.39299  7.53732334    2001_41G5 niche_data  NA
#> 2916      41G5 2002     34.39299  7.75094648    2002_41G5 niche_data  NA
#> 2917      41G5 2003     34.39299  7.74432544    2003_41G5 niche_data  NA
#> 2918      41G5 2004     34.39299  7.66013497    2004_41G5 niche_data  NA
#> 2919      41G5 2005     34.39299  8.05143985    2005_41G5 niche_data  NA
#> 2920      41G5 2006     34.39299  7.42255899    2006_41G5 niche_data  NA
#> 2921      41G5 2007     34.39299  7.46013589    2007_41G5 niche_data  NA
#> 2922      41G5 2008     34.39299  7.30024381    2008_41G5 niche_data  NA
#> 2923      41G5 2009     34.39299  7.82440918    2009_41G5 niche_data  NA
#> 2924      41G5 2010     34.39299  7.57815225    2010_41G5 niche_data  NA
#> 2925      41G5 2011     34.39299  7.07066604    2011_41G5 niche_data  NA
#> 2926      41G5 2012     34.39299  7.17843880    2012_41G5 niche_data  NA
#> 2927      41G5 2013     34.39299  7.07767122    2013_41G5 niche_data  NA
#> 2928      41G5 2014     34.39299  7.12154771    2014_41G5 niche_data  NA
#> 2929      41G5 2015     34.39299  7.14734944    2015_41G5 niche_data  NA
#> 2930      41G5 2016     34.39299  7.13405120    2016_41G5 niche_data  NA
#> 2931      41G5 2017     34.39299  7.13528823    2017_41G5 niche_data  NA
#> 2932      41G5 2018     34.39299  7.74903899    2018_41G5 niche_data  NA
#> 2933      41G5 2019     34.39299  7.41379322    2019_41G5 niche_data  NA
#> 2934      41G6 1993     32.27464  7.65958786    1993_41G6 niche_data  NA
#> 2935      41G6 1994     32.27464  7.35507010    1994_41G6 niche_data  NA
#> 2936      41G6 1995     32.27464  7.28047796    1995_41G6 niche_data  NA
#> 2937      41G6 1996     32.27464  7.22587455    1996_41G6 niche_data  NA
#> 2938      41G6 1997     32.27464  7.75848151    1997_41G6 niche_data  NA
#> 2939      41G6 1998     32.27464  7.26127803    1998_41G6 niche_data  NA
#> 2940      41G6 1999     32.27464  7.61267123    1999_41G6 niche_data  NA
#> 2941      41G6 2000     32.27464  7.30705022    2000_41G6 niche_data  NA
#> 2942      41G6 2001     32.27464  7.60571260    2001_41G6 niche_data  NA
#> 2943      41G6 2002     32.27464  7.70979607    2002_41G6 niche_data  NA
#> 2944      41G6 2004     32.27464  7.30891457    2004_41G6 niche_data  NA
#> 2945      41G6 2010     32.27464  7.10978431    2010_41G6 niche_data  NA
#> 2946      41G6 2011     32.23659  6.25036252    2011_41G6 niche_data  NA
#> 2947      41G6 2012     32.27464  7.15040203    2012_41G6 niche_data  NA
#> 2948      41G6 2013     32.19853  7.19427230    2013_41G6 niche_data  NA
#> 2949      41G6 2015     32.27464  6.89059324    2015_41G6 niche_data  NA
#> 2950      41G6 2017     32.27464  6.77858231    2017_41G6 niche_data  NA
#> 2951      41G6 2018     32.27464  7.67497859    2018_41G6 niche_data  NA
#> 2952      41G6 2019     32.27464  7.12551594    2019_41G6 niche_data  NA
#> 2953      41G7 2010     38.00000  6.23922185    2010_41G7 niche_data  NA
#> 2954      41G8 1993     39.15152  7.80294946    1993_41G8 niche_data  NA
#> 2955      41G8 2001     37.56447  7.55019106    2001_41G8 niche_data  NA
#> 2956      41G8 2002     39.02779  7.82399539    2002_41G8 niche_data  NA
#> 2957      41G8 2018     37.28142  7.29830756    2018_41G8 niche_data  NA
#> 2958      41G9 1993     59.00000  7.41879402    1993_41G9 niche_data  NA
#> 2959      41G9 1994     59.00000  7.05301770    1994_41G9 niche_data  NA
#> 2960      41G9 1995     59.00000  6.99429527    1995_41G9 niche_data  NA
#> 2961      41G9 1996     59.00000  7.12437150    1996_41G9 niche_data  NA
#> 2962      41G9 1998     59.00000  5.59297151    1998_41G9 niche_data  NA
#> 2963      41G9 1999     59.00000  7.07381712    1999_41G9 niche_data  NA
#> 2964      41G9 2001     59.00000  6.49376846    2001_41G9 niche_data  NA
#> 2965      41G9 2002     59.00000  7.00862717    2002_41G9 niche_data  NA
#> 2966      41G9 2004     59.00000  7.09680449    2004_41G9 niche_data  NA
#> 2967      41G9 2007     59.00000  6.62970039    2007_41G9 niche_data  NA
#> 2968      41G9 2009     59.00000  6.37828084    2009_41G9 niche_data  NA
#> 2969      41G9 2013     59.00000  6.95524638    2013_41G9 niche_data  NA
#> 2970      41G9 2019     59.00000  6.22208488    2019_41G9 niche_data  NA
#> 2971      41H0 1993     28.00000  7.54085392    1993_41H0 niche_data  NA
#> 2972      41H0 1994     28.00000  7.54677024    1994_41H0 niche_data  NA
#> 2973      41H0 1995     28.00000  7.50044064    1995_41H0 niche_data  NA
#> 2974      41H0 1996     28.00000  7.78747938    1996_41H0 niche_data  NA
#> 2975      41H0 1997     28.00000  7.52516227    1997_41H0 niche_data  NA
#> 2976      41H0 1998     28.00000  7.56652545    1998_41H0 niche_data  NA
#> 2977      41H0 2001     28.00000  7.74480162    2001_41H0 niche_data  NA
#> 2978      41H0 2002     28.00000  7.91847817    2002_41H0 niche_data  NA
#> 2979      41H0 2003     28.00000  7.27802471    2003_41H0 niche_data  NA
#> 2980      41H0 2004     28.00000  7.51547739    2004_41H0 niche_data  NA
#> 2981      41H0 2005     28.00000  7.54784349    2005_41H0 niche_data  NA
#> 2982      41H0 2006     28.00000  7.49917698    2006_41H0 niche_data  NA
#> 2983      41H0 2007     28.00000  7.40985642    2007_41H0 niche_data  NA
#> 2984      41H0 2009     28.00000  7.51484142    2009_41H0 niche_data  NA
#> 2985      41H0 2013     28.00000  7.75241491    2013_41H0 niche_data  NA
#> 2986      41H0 2014     28.00000  7.60765683    2014_41H0 niche_data  NA
#> 2987      41H0 2015     28.00000  7.38608069    2015_41H0 niche_data  NA
#> 2988      41H0 2019     28.00000  7.34015259    2019_41H0 niche_data  NA
#> 2989      41H1 1993     11.11676  7.67226039    1993_41H1 niche_data  NA
#> 2990      41H1 1994     11.11676  7.40334892    1994_41H1 niche_data  NA
#> 2991      41H1 1995     11.11676  7.34137785    1995_41H1 niche_data  NA
#> 2992      41H1 1996     11.11676  7.89110937    1996_41H1 niche_data  NA
#> 2993      41H1 1997     11.11676  7.29637583    1997_41H1 niche_data  NA
#> 2994      41H1 1998     11.11676  7.56440082    1998_41H1 niche_data  NA
#> 2995      41H1 1999     11.11676  7.55951397    1999_41H1 niche_data  NA
#> 2996      41H1 2000     11.11676  7.22091540    2000_41H1 niche_data  NA
#> 2997      41H1 2001     11.11676  7.49940079    2001_41H1 niche_data  NA
#> 2998      41H1 2002     11.11676  7.84510655    2002_41H1 niche_data  NA
#> 2999      41H1 2003     11.11676  7.19102042    2003_41H1 niche_data  NA
#> 3000      41H1 2004     11.11676  7.63260665    2004_41H1 niche_data  NA
#> 3001      41H1 2005     11.11676  7.25788373    2005_41H1 niche_data  NA
#> 3002      41H1 2006     11.11676  7.23212700    2006_41H1 niche_data  NA
#> 3003      41H1 2007     11.11676  7.12188686    2007_41H1 niche_data  NA
#> 3004      41H1 2008     11.11676  7.51304572    2008_41H1 niche_data  NA
#> 3005      41H1 2009     11.11676  7.43739221    2009_41H1 niche_data  NA
#> 3006      41H1 2010     11.11676  7.69574607    2010_41H1 niche_data  NA
#> 3007      41H1 2011     11.11676  7.43306156    2011_41H1 niche_data  NA
#> 3008      41H1 2012     11.11676  7.49419732    2012_41H1 niche_data  NA
#> 3009      41H1 2013     11.11676  7.55566754    2013_41H1 niche_data  NA
#> 3010      41H1 2014     11.11676  7.60086473    2014_41H1 niche_data  NA
#> 3011      41H1 2015     11.11676  7.45818187    2015_41H1 niche_data  NA
#> 3012      41H1 2016     11.11676  7.89068095    2016_41H1 niche_data  NA
#> 3013      41H1 2017     11.11676  7.63635804    2017_41H1 niche_data  NA
#> 3014      41H1 2018     11.11676  7.36072421    2018_41H1 niche_data  NA
#> 3015      41H1 2019     11.11676  7.49874547    2019_41H1 niche_data  NA
#> 3016      42G6 1993     29.09908  7.73389630    1993_42G6 niche_data  NA
#> 3017      42G6 1994     29.09908  7.42195232    1994_42G6 niche_data  NA
#> 3018      42G6 1995     29.09908  7.50306251    1995_42G6 niche_data  NA
#> 3019      42G6 1996     29.09908  7.01549591    1996_42G6 niche_data  NA
#> 3020      42G6 1997     29.09908  7.60542062    1997_42G6 niche_data  NA
#> 3021      42G6 1998     29.09908  7.61993613    1998_42G6 niche_data  NA
#> 3022      42G6 1999     29.09908  7.58934345    1999_42G6 niche_data  NA
#> 3023      42G6 2000     29.09908  7.35193473    2000_42G6 niche_data  NA
#> 3024      42G6 2004     29.09908  7.91552127    2004_42G6 niche_data  NA
#> 3025      42G6 2005     29.09908  7.87826817    2005_42G6 niche_data  NA
#> 3026      42G6 2012     29.09908  7.52284180    2012_42G6 niche_data  NA
#> 3027      42G6 2013     27.03456  7.41232609    2013_42G6 niche_data  NA
#> 3028      42G6 2015     29.09908  7.09332054    2015_42G6 niche_data  NA
#> 3029      42G7 1993     73.23766  5.55988441    1993_42G7 niche_data  NA
#> 3030      42G7 1994     72.97348  3.18922340    1994_42G7 niche_data  NA
#> 3031      42G7 1995     73.23766  4.66670488    1995_42G7 niche_data  NA
#> 3032      42G7 1996     61.65511  4.36988028    1996_42G7 niche_data  NA
#> 3033      42G7 2003     58.23156  3.49780907    2003_42G7 niche_data  NA
#> 3034      42G7 2004     65.51952  3.38180996    2004_42G7 niche_data  NA
#> 3035      42G7 2005     72.00000  4.41963278    2005_42G7 niche_data  NA
#> 3036      42G7 2006     56.11858  3.47964370    2006_42G7 niche_data  NA
#> 3037      42G7 2008     61.65511  3.61602957    2008_42G7 niche_data  NA
#> 3038      42G7 2010     70.37270  3.25399007    2010_42G7 niche_data  NA
#> 3039      42G7 2013     56.46892  3.96087132    2013_42G7 niche_data  NA
#> 3040      42G7 2015     59.72240  4.01575051    2015_42G7 niche_data  NA
#> 3041      42G7 2017     57.28530  2.74269908    2017_42G7 niche_data  NA
#> 3042      42G8 1993     30.05963  7.86838936    1993_42G8 niche_data  NA
#> 3043      42G8 1994     30.05963  8.06999630    1994_42G8 niche_data  NA
#> 3044      42G8 1995     30.05963  7.85548749    1995_42G8 niche_data  NA
#> 3045      42G8 1996     30.05963  7.66208459    1996_42G8 niche_data  NA
#> 3046      42G8 1997     30.04837  8.03324711    1997_42G8 niche_data  NA
#> 3047      42G8 1998     30.04837  7.81544158    1998_42G8 niche_data  NA
#> 3048      42G8 1999     29.27410  7.85263337    1999_42G8 niche_data  NA
#> 3049      42G8 2000     29.94156  7.55160666    2000_42G8 niche_data  NA
#> 3050      42G8 2001     29.94156  8.05420446    2001_42G8 niche_data  NA
#> 3051      42G8 2002     30.00000  7.85462486    2002_42G8 niche_data  NA
#> 3052      42G8 2003     30.00000  7.87900199    2003_42G8 niche_data  NA
#> 3053      42G8 2004     30.00000  7.82425001    2004_42G8 niche_data  NA
#> 3054      42G8 2005     30.00000  7.88728273    2005_42G8 niche_data  NA
#> 3055      42G8 2006     29.11179  7.52481547    2006_42G8 niche_data  NA
#> 3056      42G8 2007     29.83714  7.83737891    2007_42G8 niche_data  NA
#> 3057      42G8 2008     29.83714  7.73775751    2008_42G8 niche_data  NA
#> 3058      42G8 2009     29.11179  7.63860022    2009_42G8 niche_data  NA
#> 3059      42G8 2010     29.83714  7.88721276    2010_42G8 niche_data  NA
#> 3060      42G8 2011     29.43642  7.66552653    2011_42G8 niche_data  NA
#> 3061      42G8 2012     29.83714  7.47061622    2012_42G8 niche_data  NA
#> 3062      42G8 2013     29.88312  7.65452095    2013_42G8 niche_data  NA
#> 3063      42G8 2014     29.94156  7.47670101    2014_42G8 niche_data  NA
#> 3064      42G8 2015     28.88192  7.47202489    2015_42G8 niche_data  NA
#> 3065      42G8 2016     29.83714  7.60266949    2016_42G8 niche_data  NA
#> 3066      42G8 2017     28.70168  7.29741089    2017_42G8 niche_data  NA
#> 3067      42G8 2018     30.00000  7.70575883    2018_42G8 niche_data  NA
#> 3068      42G8 2019     30.00000  7.61437571    2019_42G8 niche_data  NA
#> 3069      42H0 1993     43.82713  7.55470842    1993_42H0 niche_data  NA
#> 3070      42H0 1994     43.82713  7.57355889    1994_42H0 niche_data  NA
#> 3071      42H0 1995     43.82713  7.47107857    1995_42H0 niche_data  NA
#> 3072      42H0 1996     43.82713  7.91582332    1996_42H0 niche_data  NA
#> 3073      42H0 1998     43.82713  7.60109615    1998_42H0 niche_data  NA
#> 3074      42H1 1993     18.30025  7.98388131    1993_42H1 niche_data  NA
#> 3075      42H1 1994     18.30025  7.73413010    1994_42H1 niche_data  NA
#> 3076      42H1 1995     18.30025  7.53345711    1995_42H1 niche_data  NA
#> 3077      42H1 1996     18.30025  7.94023218    1996_42H1 niche_data  NA
#> 3078      42H1 1997     18.30025  7.45455444    1997_42H1 niche_data  NA
#> 3079      42H1 1998     18.30025  7.78536054    1998_42H1 niche_data  NA
#> 3080      42H1 1999     18.30025  7.65605818    1999_42H1 niche_data  NA
#> 3081      42H1 2000     18.30025  7.38661038    2000_42H1 niche_data  NA
#> 3082      42H1 2001     18.30025  7.68775677    2001_42H1 niche_data  NA
#> 3083      42H1 2002     18.30025  8.01096340    2002_42H1 niche_data  NA
#> 3084      42H1 2003     18.30025  7.37061505    2003_42H1 niche_data  NA
#> 3085      42H1 2004     18.30025  7.66457465    2004_42H1 niche_data  NA
#> 3086      42H1 2005     18.30025  7.50144000    2005_42H1 niche_data  NA
#> 3087      42H1 2006     18.30025  7.52218130    2006_42H1 niche_data  NA
#> 3088      42H1 2007     18.30025  7.60482170    2007_42H1 niche_data  NA
#> 3089      42H1 2008     18.30025  7.71537788    2008_42H1 niche_data  NA
#> 3090      42H1 2009     18.30025  7.72670913    2009_42H1 niche_data  NA
#> 3091      42H1 2010     18.30025  7.91550677    2010_42H1 niche_data  NA
#> 3092      42H1 2011     18.30025  7.61017497    2011_42H1 niche_data  NA
#> 3093      42H1 2012     18.30025  7.66729048    2012_42H1 niche_data  NA
#> 3094      42H1 2013     18.30025  7.70589821    2013_42H1 niche_data  NA
#> 3095      42H1 2014     18.30025  7.65363411    2014_42H1 niche_data  NA
#> 3096      42H1 2015     18.30025  7.54550955    2015_42H1 niche_data  NA
#> 3097      42H1 2016     18.30025  7.99614969    2016_42H1 niche_data  NA
#> 3098      42H1 2017     18.30025  7.79599645    2017_42H1 niche_data  NA
#> 3099      42H1 2018     18.30025  7.55210058    2018_42H1 niche_data  NA
#> 3100      42H1 2019     18.30025  7.56149286    2019_42H1 niche_data  NA
#> 3101      43G6 1993     30.91015  7.59425001    1993_43G6 niche_data  NA
#> 3102      43G6 1994     30.91015  6.98145891    1994_43G6 niche_data  NA
#> 3103      43G6 1995     30.91015  7.42949538    1995_43G6 niche_data  NA
#> 3104      43G6 1996     30.91015  7.09143358    1996_43G6 niche_data  NA
#> 3105      43G6 1998     30.91015  6.90988165    1998_43G6 niche_data  NA
#> 3106      43G6 2004     30.91015  7.77762803    2004_43G6 niche_data  NA
#> 3107      43G6 2013     30.91015  6.91941191    2013_43G6 niche_data  NA
#> 3108      43G6 2014     30.91015  6.70009889    2014_43G6 niche_data  NA
#> 3109      43G6 2016     30.91015  7.66555512    2016_43G6 niche_data  NA
#> 3110      43G6 2017     30.91015  6.90064678    2017_43G6 niche_data  NA
#> 3111      43G6 2018     30.91015  7.40607598    2018_43G6 niche_data  NA
#> 3112      43G7 1999     56.05859  5.60292947    1999_43G7 niche_data  NA
#> 3113      43G7 2003     52.91734  5.38141735    2003_43G7 niche_data  NA
#> 3114      43G7 2004     55.36378  6.63740292    2004_43G7 niche_data  NA
#> 3115      43G7 2006     54.28073  4.69075291    2006_43G7 niche_data  NA
#> 3116      43G7 2012     55.69919  6.27523528    2012_43G7 niche_data  NA
#> 3117      43G7 2013     55.25524  5.22319414    2013_43G7 niche_data  NA
#> 3118      43G7 2015     52.67770  5.20981991    2015_43G7 niche_data  NA
#> 3119      43G8 2001     27.22653  8.03733456    2001_43G8 niche_data  NA
#> 3120      43G8 2002     27.22653  7.84996093    2002_43G8 niche_data  NA
#> 3121      43G8 2005     27.22653  7.90168527    2005_43G8 niche_data  NA
#> 3122      43G9 1996     66.36603  5.76541124    1996_43G9 niche_data  NA
#> 3123      43G9 1998     65.81358  4.31112624    1998_43G9 niche_data  NA
#> 3124      43G9 2001     65.81358  4.99153745    2001_43G9 niche_data  NA
#> 3125      43G9 2002     66.08981  6.66818526    2002_43G9 niche_data  NA
#> 3126      43G9 2004     66.36603  3.44164284    2004_43G9 niche_data  NA
#> 3127      43G9 2006     61.90562  4.17563407    2006_43G9 niche_data  NA
#> 3128      43G9 2012     65.81358  5.05298159    2012_43G9 niche_data  NA
#> 3129      43G9 2014     66.08981  4.49921251    2014_43G9 niche_data  NA
#> 3130      43G9 2015     61.39362  4.26680143    2015_43G9 niche_data  NA
#> 3131      43G9 2016     62.41762  3.96178732    2016_43G9 niche_data  NA
#> 3132      43G9 2019     65.81358  4.75566628    2019_43G9 niche_data  NA
#> 3133      43H0 1993     52.76306  7.67080781    1993_43H0 niche_data  NA
#> 3134      43H0 1994     52.76306  7.48433354    1994_43H0 niche_data  NA
#> 3135      43H0 1995     52.76306  7.43212180    1995_43H0 niche_data  NA
#> 3136      43H0 1996     52.76306  7.87986123    1996_43H0 niche_data  NA
#> 3137      43H0 2005     52.76306  7.52695544    2005_43H0 niche_data  NA
#> 3138      43H0 2006     52.76306  7.29674344    2006_43H0 niche_data  NA
#> 3139      43H0 2009     52.76306  7.13575589    2009_43H0 niche_data  NA
#> 3140      43H0 2015     52.76306  7.40447357    2015_43H0 niche_data  NA
#> 3141      43H1 1993     23.00000  7.70678900    1993_43H1 niche_data  NA
#> 3142      43H1 1994     23.00000  7.50578169    1994_43H1 niche_data  NA
#> 3143      43H1 1995     23.00000  7.44672206    1995_43H1 niche_data  NA
#> 3144      43H1 1996     23.00000  7.92396006    1996_43H1 niche_data  NA
#> 3145      43H1 2000     23.00000  7.24706226    2000_43H1 niche_data  NA
#> 3146      43H1 2004     23.00000  7.56359968    2004_43H1 niche_data  NA
#> 3147      43H1 2007     23.00000  7.47169877    2007_43H1 niche_data  NA
#> 3148      43H1 2008     23.00000  7.63645153    2008_43H1 niche_data  NA
#> 3149      43H1 2016     23.00000  7.90658215    2016_43H1 niche_data  NA
#> 3150      44G6 1993     50.69443  7.68461121    1993_44G6 niche_data  NA
#> 3151      44G6 1994     50.69443  7.15367464    1994_44G6 niche_data  NA
#> 3152      44G6 1995     50.69443  7.82556761    1995_44G6 niche_data  NA
#> 3153      44G6 1996     50.69443  7.68534888    1996_44G6 niche_data  NA
#> 3154      44G6 1997     50.69443  7.92742019    1997_44G6 niche_data  NA
#> 3155      44G6 1998     50.69443  7.66305466    1998_44G6 niche_data  NA
#> 3156      44G6 1999     50.69443  7.54103293    1999_44G6 niche_data  NA
#> 3157      44G6 2000     50.69443  7.62919222    2000_44G6 niche_data  NA
#> 3158      44G6 2001     50.69443  7.55328329    2001_44G6 niche_data  NA
#> 3159      44G6 2002     50.69443  7.86135257    2002_44G6 niche_data  NA
#> 3160      44G6 2003     50.69443  7.70986296    2003_44G6 niche_data  NA
#> 3161      44G6 2004     50.69443  7.94261685    2004_44G6 niche_data  NA
#> 3162      44G6 2005     50.69443  7.79756388    2005_44G6 niche_data  NA
#> 3163      44G6 2006     50.69443  6.90588567    2006_44G6 niche_data  NA
#> 3164      44G6 2007     50.69443  7.54885020    2007_44G6 niche_data  NA
#> 3165      44G6 2008     50.69443  7.04478808    2008_44G6 niche_data  NA
#> 3166      44G6 2009     50.69443  8.13603276    2009_44G6 niche_data  NA
#> 3167      44G6 2010     50.69443  7.28397441    2010_44G6 niche_data  NA
#> 3168      44G6 2011     50.69443  5.91376869    2011_44G6 niche_data  NA
#> 3169      44G6 2012     50.69443  7.64334032    2012_44G6 niche_data  NA
#> 3170      44G6 2013     50.69443  6.54638622    2013_44G6 niche_data  NA
#> 3171      44G6 2014     50.69443  6.45347120    2014_44G6 niche_data  NA
#> 3172      44G6 2015     50.69443  5.36760772    2015_44G6 niche_data  NA
#> 3173      44G6 2016     50.69443  7.57570168    2016_44G6 niche_data  NA
#> 3174      44G6 2017     50.69443  6.76077353    2017_44G6 niche_data  NA
#> 3175      44G6 2018     50.69443  7.65040617    2018_44G6 niche_data  NA
#> 3176      44G6 2019     50.69443  7.46095143    2019_44G6 niche_data  NA
#> 3177      44G7 1993     83.47453  5.73880798    1993_44G7 niche_data  NA
#> 3178      44G7 1994     83.19037  3.55642896    1994_44G7 niche_data  NA
#> 3179      44G7 1995     82.63989  5.25981280    1995_44G7 niche_data  NA
#> 3180      44G7 1996     81.77901  3.96215107    1996_44G7 niche_data  NA
#> 3181      44G7 1997     81.03609  4.80467277    1997_44G7 niche_data  NA
#> 3182      44G7 1998     81.03609  4.64660676    1998_44G7 niche_data  NA
#> 3183      44G7 1999     81.46861  4.29224988    1999_44G7 niche_data  NA
#> 3184      44G7 2000     81.46861  6.06195513    2000_44G7 niche_data  NA
#> 3185      44G7 2001     80.60356  4.30420686    2001_44G7 niche_data  NA
#> 3186      44G7 2002     81.03609  5.25261056    2002_44G7 niche_data  NA
#> 3187      44G7 2003     77.01854  4.71610538    2003_44G7 niche_data  NA
#> 3188      44G7 2004     81.03609  4.65323357    2004_44G7 niche_data  NA
#> 3189      44G7 2005     81.77901  6.15668864    2005_44G7 niche_data  NA
#> 3190      44G7 2006     72.53629  4.41542022    2006_44G7 niche_data  NA
#> 3191      44G7 2007     80.60356  4.50387367    2007_44G7 niche_data  NA
#> 3192      44G7 2008     81.03609  4.20145782    2008_44G7 niche_data  NA
#> 3193      44G7 2009     81.03609  5.57961805    2009_44G7 niche_data  NA
#> 3194      44G7 2010     80.43781  4.41419233    2010_44G7 niche_data  NA
#> 3195      44G7 2011     80.43781  3.54937987    2011_44G7 niche_data  NA
#> 3196      44G7 2012     81.03609  4.95336450    2012_44G7 niche_data  NA
#> 3197      44G7 2013     80.60356  3.53202611    2013_44G7 niche_data  NA
#> 3198      44G7 2014     81.03609  4.08567790    2014_44G7 niche_data  NA
#> 3199      44G7 2015     72.08430  3.27012011    2015_44G7 niche_data  NA
#> 3200      44G7 2016     80.60356  4.11482901    2016_44G7 niche_data  NA
#> 3201      44G7 2017     80.43781  3.49388255    2017_44G7 niche_data  NA
#> 3202      44G7 2018     81.03609  4.48265880    2018_44G7 niche_data  NA
#> 3203      44G7 2019     82.63989  4.20275734    2019_44G7 niche_data  NA
#> 3204      44G8 1993     27.80362  7.72392169    1993_44G8 niche_data  NA
#> 3205      44G8 1995     27.80362  7.62250144    1995_44G8 niche_data  NA
#> 3206      44G8 1996     27.80362  7.60706400    1996_44G8 niche_data  NA
#> 3207      44G8 1997     27.54758  7.70949797    1997_44G8 niche_data  NA
#> 3208      44G8 1998     27.54758  7.77212630    1998_44G8 niche_data  NA
#> 3209      44G8 1999     27.54758  7.56527501    1999_44G8 niche_data  NA
#> 3210      44G8 2000     27.54758  7.63720782    2000_44G8 niche_data  NA
#> 3211      44G8 2001     27.00000  8.01716905    2001_44G8 niche_data  NA
#> 3212      44G8 2002     27.54758  7.74000147    2002_44G8 niche_data  NA
#> 3213      44G8 2003     26.97803  7.56442483    2003_44G8 niche_data  NA
#> 3214      44G8 2004     27.00000  7.83413962    2004_44G8 niche_data  NA
#> 3215      44G8 2005     27.67238  7.65312705    2005_44G8 niche_data  NA
#> 3216      44G8 2006     26.97803  7.21979521    2006_44G8 niche_data  NA
#> 3217      44G8 2007     26.97803  7.24528532    2007_44G8 niche_data  NA
#> 3218      44G8 2008     27.00000  7.50773159    2008_44G8 niche_data  NA
#> 3219      44G8 2009     26.97803  7.32158972    2009_44G8 niche_data  NA
#> 3220      44G8 2010     27.00000  7.96554871    2010_44G8 niche_data  NA
#> 3221      44G8 2011     26.97803  7.86155967    2011_44G8 niche_data  NA
#> 3222      44G8 2012     27.00000  7.46068614    2012_44G8 niche_data  NA
#> 3223      44G8 2013     27.00000  7.62878594    2013_44G8 niche_data  NA
#> 3224      44G8 2014     27.00000  7.70787402    2014_44G8 niche_data  NA
#> 3225      44G8 2015     27.00000  7.49783467    2015_44G8 niche_data  NA
#> 3226      44G8 2016     27.00000  7.65435180    2016_44G8 niche_data  NA
#> 3227      44G8 2017     26.56589  7.60768734    2017_44G8 niche_data  NA
#> 3228      44G8 2018     27.00000  7.51447770    2018_44G8 niche_data  NA
#> 3229      44G8 2019     26.97803  7.44794445    2019_44G8 niche_data  NA
#> 3230      44G9 1996     46.52287  6.84888951    1996_44G9 niche_data  NA
#> 3231      44G9 2002     46.52287  7.92068257    2002_44G9 niche_data  NA
#> 3232      44G9 2003     46.17992  7.17043670    2003_44G9 niche_data  NA
#> 3233      44G9 2007     46.02985  5.89811967    2007_44G9 niche_data  NA
#> 3234      44G9 2008     45.98064  6.87075240    2008_44G9 niche_data  NA
#> 3235      44H0 1993     56.07331  7.43251699    1993_44H0 niche_data  NA
#> 3236      44H0 1994     56.07331  6.95833773    1994_44H0 niche_data  NA
#> 3237      44H0 1995     56.07331  7.07065140    1995_44H0 niche_data  NA
#> 3238      44H0 1996     56.07331  7.54987979    1996_44H0 niche_data  NA
#> 3239      44H0 1997     56.07331  7.17430696    1997_44H0 niche_data  NA
#> 3240      44H0 1998     56.07331  6.62525222    1998_44H0 niche_data  NA
#> 3241      44H0 1999     56.07331  6.75264530    1999_44H0 niche_data  NA
#> 3242      44H0 2000     56.07331  6.93494539    2000_44H0 niche_data  NA
#> 3243      44H0 2001     56.07331  6.56063747    2001_44H0 niche_data  NA
#> 3244      44H0 2002     56.07331  6.81166376    2002_44H0 niche_data  NA
#> 3245      44H0 2003     56.07331  6.06552475    2003_44H0 niche_data  NA
#> 3246      44H0 2004     56.07331  7.04603485    2004_44H0 niche_data  NA
#> 3247      44H0 2005     56.07331  6.91532677    2005_44H0 niche_data  NA
#> 3248      44H0 2006     56.07331  6.42195804    2006_44H0 niche_data  NA
#> 3249      44H0 2007     56.07331  6.62464985    2007_44H0 niche_data  NA
#> 3250      44H0 2008     56.07331  7.29359075    2008_44H0 niche_data  NA
#> 3251      44H0 2011     56.07331  6.71013207    2011_44H0 niche_data  NA
#> 3252      44H0 2012     56.07331  7.35145920    2012_44H0 niche_data  NA
#> 3253      44H0 2013     56.07331  7.11978550    2013_44H0 niche_data  NA
#> 3254      44H0 2014     56.07331  6.65335013    2014_44H0 niche_data  NA
#> 3255      44H0 2016     56.07331  6.88567812    2016_44H0 niche_data  NA
#> 3256      44H0 2017     56.07331  7.55688774    2017_44H0 niche_data  NA
#> 3257      44H0 2018     56.07331  5.63771488    2018_44H0 niche_data  NA
#> 3258      44H1 1993     30.23161  7.66812010    1993_44H1 niche_data  NA
#> 3259      44H1 1994     30.23161  7.48463470    1994_44H1 niche_data  NA
#> 3260      44H1 1995     30.23161  7.64794919    1995_44H1 niche_data  NA
#> 3261      44H1 1996     30.23161  7.87837719    1996_44H1 niche_data  NA
#> 3262      44H1 1999     30.23161  7.83634315    1999_44H1 niche_data  NA
#> 3263      44H1 2001     30.23161  7.62386028    2001_44H1 niche_data  NA
#> 3264      44H1 2002     30.23161  8.11188027    2002_44H1 niche_data  NA
#> 3265      44H1 2003     30.23161  7.45708704    2003_44H1 niche_data  NA
#> 3266      44H1 2004     30.23161  7.60755997    2004_44H1 niche_data  NA
#> 3267      44H1 2006     30.23161  7.61031516    2006_44H1 niche_data  NA

all_dat <- all_dat %>%
  drop_na(n) %>%
  mutate(small_sample = ifelse(n < 30, "Y", "N"))# %>% filter(small_sample == "N")
#> drop_na: removed 1,535 rows (47%), 1,732 rows remaining
#> mutate: new variable 'small_sample' (character) with 2 unique values and 0% NA

# Depth
ggplot(all_dat %>% filter(year == 1999)) +
  geom_bar(aes(ices_rect, median_depth, fill = type), position="dodge", stat="identity") +
  theme(axis.text.x = element_text(angle = 90)) +
  facet_wrap(~small_sample, ncol = 1) +
  NULL
#> filter: removed 1,690 rows (98%), 42 rows remaining


# Oxygen
ggplot(all_dat %>% filter(year == 1999)) +
  geom_bar(aes(ices_rect, median_oxy, fill = type), position="dodge", stat="identity") +
  theme(axis.text.x = element_text(angle = 90)) +
  facet_wrap(~small_sample, ncol = 1) +
  NULL
#> filter: removed 1,690 rows (98%), 42 rows remaining


# The fine scale variables are matched to haul. However, the ices_rect level variables
# and sub division averages should not be averages of the hauls in those areas, but rather
# average prediction on those levels. To achieve that, I will join in those averages from the pred_grid
knitr::knit_exit()